In our Rust in Production interview series, we talk with developers and technical leaders who are shaping how Rust is built and used in practice.

This interview explores JetBrains’ strategy for supporting the Rust Foundation and collaborating around shared tooling like rust-analyzer, the rationale behind launching RustRover, and how user adoption data shapes priorities such as debugging, async Rust workflows, and test tooling (including cargo nextest).

Today’s guest is the Head of the Rust Ecosystem at JetBrains, Vitaly Bragilevsky.

In talks and interviews you often emphasize JetBrains’ long-term commitment to the Rust ecosystem, including your involvement in the Rust Foundation and collaboration around rust-analyzer. How do you balance building proprietary JetBrains tooling with contributing to shared, community-owned Rust infrastructure in a way that actually accelerates Rust adoption rather than fragmenting the tooling landscape?

We think about this balance very deliberately, because the last thing the Rust ecosystem needs is artificial fragmentation driven by vendors pulling in different directions.

First, it’s important to be precise about our role. We don’t directly contribute code to core Rust open-source projects like rust-analyzer, but we very much share the same underlying problems. Because of that, we stay in close communication with the rust-analyzer team, exchange feedback, and align in direction where it makes sense. In parallel, we participate in Rust Foundation programs focused on supporting the people and processes behind Rust development, not on controlling technology.

From a product perspective, our default stance is: use what the Rust ecosystem already provides whenever possible. We rely on the standard Rust toolchain and regularly evaluate which existing components can be reused or integrated into RustRover rather than reinventing them. This helps us stay compatible with how Rust developers already work and lowers the cognitive cost of adopting the language.

Regarding fragmentation, I see it a bit differently. Diversity of tools and approaches isn’t a failure mode by default — it’s often a strength. Different developers, teams, and domains need different workflows. A strictly limited, highly opinionated tooling setup may be elegant, but it can also exclude people. Healthy competition and multiple well-integrated tools give users real choice, and that choice is what ultimately accelerates adoption.

Our goal is not to replace or overshadow community-owned infrastructure, but to build on top of it and around it: providing a polished, integrated experience for users who value that, while staying aligned with the broader ecosystem. When developers can pick the tools that fit them best — whether that’s lightweight editors, full IDEs, or something in between — Rust becomes more accessible, not more fragmented.

RustRover is JetBrains’ first dedicated Rust IDE after years of offering Rust support as plugins for IntelliJ IDEA and CLion. From your perspective as Head of the Rust Ecosystem, what concrete signs inside JetBrains and in the wider community convinced you that the time had come to invest in a standalone Rust product rather than “just” improving the plugins?

The decision was driven by a combination of external signals from the Rust ecosystem and very practical internal considerations at JetBrains.

Externally, we saw sustained, long-term growth of Rust — not just in developer adoption, but in serious production use by companies across very different industries. More teams were betting on Rust for core systems, making the ecosystem commercially relevant in a way that clearly went beyond enthusiasts and early adopters. At that point, simply treating Rust as an add-on to other IDEs no longer reflected how important it had become for many users.

Internally, having a standalone product matters a lot. It allows us to put Rust development on a clear roadmap, dedicate a focused team, and invest in the experience end-to-end rather than competing for attention and resources within a broader product. From an organizational and product-management perspective, this is a much healthier way to build something long-term.

There’s also a signaling effect that I personally consider very important. When JetBrains launches a dedicated, commercial IDE for a language, it sends a strong message to companies: this technology is mature, well-supported, and a safe bet. In that sense, RustRover is not only a response to Rust’s growth — it’s also a way of reinforcing it, giving teams additional confidence that Rust is ready for broader adoption in professional environments.

JetBrains surveys show a steady rise in Rust usage and in the popularity of advanced IDE tooling such as rust-analyzer and IntelliJ Rust. How is this data influencing your internal roadmap: which Rust adoption patterns among your users most directly shape what your team prioritizes in RustRover and related tools?

For us, surveys are not just about measuring popularity — they’re a way to understand how Rust is actually used in practice and where developers are still paying a lot of friction tax.

The most valuable signals for us are things like the industries where Rust is being adopted, the kinds of applications people are building, and the tooling problems they repeatedly run into. These answers have a very direct impact on our roadmap, because they tell us where better tooling can realistically move the needle for adoption and productivity.

A concrete example is debugging. We consistently see that many Rust developers avoid debuggers altogether, not because they don’t need them, but because the existing experience is often unreliable or hard to use. That’s a clear signal for us to invest more heavily in debugger quality and integration, rather than assuming that “Rust developers just don’t debug.”

We also see that a large share of Rust development today is backend work, with heavy use of asynchronous Rust. That has consequences for everything from code insight and diagnostics to debugging and profiling, and it means we need to focus on improving the developer experience specifically for async-heavy scenarios, not just for small libraries or toy examples.

Finally, the surveys show a significant cluster of Rust usage in areas like blockchain — for example, ecosystems such as Solana. For us, this isn’t a niche curiosity; it’s a signal that real teams are building production systems there, and that investing in better support for these workflows can have a tangible impact. In that sense, our roadmap is shaped less by abstract ideas of what Rust could be used for, and more by careful observation of what Rust developers are already doing today — and where better tools can help them do it with less friction.

For someone who uses Zed/Neovim with a rust-analyzer, what difference would you feel with RustRover. Is the proprietary JB engine better than the tools rust provides and what’s the story for a proprietary engine instead of a community-driven analyzer?

If you’re coming from Zed or Neovim with rust-analyzer, the first difference you’ll notice is that RustRover is not “just” code analysis — it’s a full IDE experience that’s available out of the box and designed to work as a coherent system.

RustRover combines Rust code analysis with a lot of other IDE capabilities: a debugger, a profiler, advanced dependency management, collaboration tools, support for web technologies and databases, and AI features ranging from simple model-based interactions to more advanced agent-style workflows. On the debugging side in particular, we use our own forks of LLDB and GDB, tuned specifically for Rust, because upstream debuggers still struggle with many Rust-specific constructs.

I usually try not to frame this as a direct “rust-analyzer vs. JetBrains engine” comparison. Both analyzers cover a broadly similar feature set, and both have rough edges — Rust is a very complex language, and large real-world codebases stress tools in different ways. Depending on project size, architecture, macros, build setup, and many other factors, developers can get noticeably different results from different analyzers.

Our engine has a long history. It started about ten years ago as part of the IntelliJ Rust plugin, well before rust-analyzer existed, and it was built using the traditional JetBrains approach to deep IDE integration. Interestingly, it was originally started by Aleksey Kladov (matklad) — the same person who later initiated rust-analyzer, which is based on very different architectural principles.

Today, we don’t see a strong reason to abandon our own analysis stack. One major advantage is that we’re much closer to the IDE itself: we’re not constrained by the LSP protocol, and we can build UX features that simply aren’t possible when the analyzer is a separate, generic service. That tight integration enables things like richer refactorings, more context-aware navigation, and smoother interactions across debugging, profiling, and code insight.

Finally, I actually think it’s healthy that Rust has two serious analyzers. It means no one can afford to be complacent. The competition pushes both approaches forward — and in the end, Rust developers are the ones who benefit from that constant pressure to improve.

JetBrains actively supports the Rust Foundation. What motivated this decision, and what kind of value does JetBrains expect to get back?

Joining the Rust Foundation was a very natural step for us at the time. As Rust was entering a more mature phase of adoption, the Foundation emerged as a focal point for coordinating long-term, ecosystem-level efforts: supporting core infrastructure, improving the sustainability of key projects, investing in developer education, and providing a neutral space where companies and the community can work together.

For JetBrains, participation in the Rust Foundation gives us a structured and transparent way to engage at that level. We get the opportunity to talk directly with companies that are deeply invested in Rust, to contribute to discussions about priorities and initiatives, and to propose or support programs that improve the overall developer experience. While the Foundation doesn’t dictate technical direction, it plays an important role in aligning efforts around shared problems that no single company can solve alone.

From a practical standpoint, working with an organization like the Rust Foundation is also simply more convenient and scalable for us as a company. We still communicate with individual open-source projects and with members of the Rust community directly, but the Foundation gives us a central forum where those conversations can happen more systematically and with broader impact.

Ultimately, the value we expect to get back is not a specific technical advantage, but a healthier, more sustainable Rust ecosystem. That directly benefits our users — and, by extension, our products — because better infrastructure, better-supported maintainers, and clearer long-term signals make Rust a safer and more attractive choice for teams and companies.

There is a fairly common perception in the community that testing in Rust can feel less flexible and more verbose — especially when it comes to mocks, fixtures, and test infrastructure, which often rely on third-party crates and careful architectural design. Do you agree that this is a real pain point for Rust developers today? How do you see this area evolving, and is improving test ergonomics something that the language team and tooling vendors like JetBrains are actively focusing on?

I think cargo nextest is a great example of how the Rust ecosystem is evolving to address real testing pain points without overloading the language itself. Rust deliberately keeps its built-in testing model minimal and reliable, but that means that questions of scale – performance, isolation, flaky tests, CI ergonomics – are pushed into external tooling. As projects grow, cargo test often becomes a bottleneck, and that’s exactly the space where nextest provides a much more robust and production-ready test execution model.

What’s important is that nextest doesn’t change how tests are written in Rust at all. All the existing approaches – standard #[test], async tests, fixtures and mocks from third-party crates – continue to work as they are. Instead, it focuses on execution, observability, and control, which are some of the biggest sources of friction for teams working with large Rust codebases. In that sense, it complements the existing ecosystem rather than competing with it.

From a tooling perspective, this is exactly where IDEs can add a lot of value. We see strong demand for better test workflows, and that’s why we’re actively working on deeper integration of cargo nextest into RustRover — including running, debugging, and visualizing test results. Our goal is to hide as much of the infrastructural complexity as possible behind a coherent UX, so developers can benefit from powerful tools like nextest without having to constantly think about how all the pieces are wired together.

Every blockchain user cares about fast and secure transactions and wants to have the flexibility to work with different cryptocurrencies from the same interface.

The primary goal of blockchain development today is achieving near-instant cryptocurrency transaction speeds. For that, a blockchain must be scalable, meaning capable of handling a high volume of transactions without compromising security or decentralization.

Another important aspect is blockchain interoperability-the ability of different blockchain networks to communicate, share data, and interact with one another, providing users with secure cross-blockchain functionalities.

In this blog, we examine the various challenges related to blockchain scalability and interoperability and the proposed solutions.

What is blockchain scalability?

Scalability is the ability of a blockchain network to handle an increasing number of transactions and users efficiently. It involves enhancing the network’s capacity to process transactions quickly and cost-effectively while maintaining security and decentralization. Scalability is a critical factor in blockchain networks because the size and complexity of the network grow with each transaction added to the chain.

When a network cannot handle the transaction load, it results in slow processing times and a poor user experience. These issues are especially important for applications requiring high transaction volumes, such as decentralized finance (DeFi) and supply chain management. Therefore, scalability is essential for the future growth of blockchain technology.

How to improve scalability in blockchain?

Achieving high transactions per second (TPS) rate requires nodes to confirm blocks quickly, following specific guidelines that define valid transactions and determine who can process and add them to the blockchain.

Larger block sizes can enhance transactional capacity by enabling more transactions to be included in a block, allowing the entire blockchain to keep up with increasing data usage and exchange.

Beyond speed and size, the potential for future innovation is a key characteristic of a scalable blockchain. Whether in business, healthcare, or education, scalable blockchains can meet users’ evolving needs by adapting the technologies they rely on daily.

Elements of a scalable blockchain

Blockchain scalability is assessed based on five criteria:

Capacity

Recording data differs from storing it, and nodes can optimize and prioritize either or both tasks to meet the clients’ needs. Each node can handle large data volumes if it has sufficient storage capacity.

Networking

Most of the required network bandwidth is distributed evenly between block confirmations. When a block is discovered, nodes work to transmit the block data across the blockchain. This extensive use of network resources can cause delays unless efficient data transmission mechanisms are in place.

Throughput

Blockchain throughput refers to the block size and the time needed to confirm a transaction within that block. A bigger size block can increase the number of transactions it processes, verifies, records, and stores.

Finality

Blockchain finality is the point at which a transaction is recorded on the blockchain and considered irreversible, ensuring that it cannot be altered or undone. This concept guarantees the security and integrity of transactions within the blockchain network.

Confirmation time

Confirmation time is the average duration between submitting a transaction to the network and recording it as a verified block. Faster block generation and quicker transaction confirmations directly contribute to higher transaction throughput and increased user satisfaction.

What is the blockchain scalability trilemma?

The Blockchain Scalability Trilemma is a concept introduced by Vitalik Buterin, the co-founder of Ethereum. It refers to the challenge in blockchain development of achieving three key attributes simultaneously: decentralization, security, and scalability. The trilemma suggests that it is difficult, if not impossible, for a blockchain to optimize all three points at the same time. Here’s a breakdown of each attribute:

1. Decentralization: This refers to the extent to which a blockchain is operated by a distributed network of nodes, rather than being controlled by a single entity or a small group of entities. High decentralization means more nodes participate in validating and verifying transactions, making the network more resilient and less susceptible to attacks or failures.
2. Security: Security ensures that the blockchain is resistant to attacks and that transactions are secure and immutable. A highly secure blockchain prevents double-spending, protects user data, and maintains the integrity of the network even in the presence of malicious actors.
3. Scalability: Scalability refers to the blockchain’s ability to handle an increasing number of transactions and users without compromising performance. A scalable blockchain can process more transactions per second (TPS) and support a growing number of applications and use cases.

The trilemma posits that improving one or two of these attributes often comes at the expense of the third. For example:

• Improving scalability: Increasing the number of transactions per second might require larger blocks or faster block building times, which could reduce decentralization by making it harder for smaller nodes to participate or by requiring more powerful hardware. This could also impact security if the changes make it easier for malicious actors to compromise the network.
• Enhancing security: To enhance security, a blockchain might use more complex consensus mechanisms or cryptographic techniques, which can increase the computational requirements and slow down transaction processing, thus reducing scalability.
• Increasing decentralization: Ensuring that more nodes can participate in the network can make consensus slower and more resource-intensive, impacting both scalability and potentially security if the network becomes more vulnerable to certain types of attacks.

Blockchain scalability solutions

Developers and researchers in the blockchain space are continuously working on innovative solutions to address the scalability trilemma. These solutions aim to achieve a balance between decentralization, security, and scalability. Some of the approaches include:

1. Layer 1 solutions

First-layer solutions involve changes to the main blockchain network’s codebase and are therefore also known as on-chain scaling. Layer 1 solutions focus on enhancing the core features of the blockchain network, such as increasing the block size limit or reducing block verification time. Popular layer 1 scalability solutions include sharding, Segregated Witness (SEGWIT), and hard forking.

Sharding

Sharding divides the blockchain network into smaller, more manageable parts called shards that operate in parallel, each processing its own set of transactions. This division increases the overall processing capacity of the network, as its performance becomes the sum of multiple parallel transactions. Sharding effectively eliminates the dependence on individual node speed for improved transaction throughput.

Segregated witness (SEGWIT)

SEGWIT is a protocol improvement in the Bitcoin blockchain that changes the way data is stored. By removing the signature data associated with each transaction, SEGWIT frees up space to store more transactions. Digital signatures, which verify the ownership and availability of the sender’s funds, take up about 70% of the transaction space. Removing this data increases the capacity for additional transactions.

2. Layer 2 scalability solutions

Layer 2 scalability solutions are designed to address the scalability challenges by building additional layers on top of the existing blockchain infrastructure. On this layer, intermediate transactions can be performed and validated in bulk, thus enhancing the throughput and transaction speeds of blockchain networks while maintaining their security and decentralization.

Sidechains

Sidechains are separate chains that run parallel to the main blockchain, allowing for increased throughput and scalability. They can be used for specific applications or use cases, enabling faster transaction speeds while still being connected to the main chain for security.

Examples of sidechains include:

• Liquid Network (Bitcoin): A sidechain-based settlement network for traders and exchanges, providing faster and more confidential transactions.
• Rootstock (RSK): A smart contract platform that is connected to the Bitcoin blockchain via a two-way peg, enabling smart contracts with Bitcoin’s security.

State channels

State channels allow participants to conduct a series of off-chain transactions that are only recorded on the main chain when necessary. This reduces network congestion and improves transaction speeds. Examples include:

• Raiden Network (Ethereum): A payment channel network for Ethereum, similar to the Lightning Network for Bitcoin.
• Celer Network: A layer 2 scaling platform that uses state channels and other techniques to achieve high throughput and low latency.

Rollups

Rollups are a layer 2 scaling solution that involves bundling multiple transactions into a single transaction, which is then submitted to the main chain. Rollups can be either optimistic or zero-knowledge (zk-rollups):

• Optimistic rollups: Assume transactions are valid by default and only run computations in case of a dispute. Examples include Optimism and Arbitrum.
• zk-rollups: Use zero-knowledge proofs to ensure the validity of transactions, reducing the computational load on the main chain. Examples include zkSync and Loopring.

Plasma

Plasma is a protocol that creates decentralized applications within sidechains, allowing for increased scalability by processing transactions separately from the main chain while still being anchored to it for security.

Interoperability protocols

These protocols aim to bridge different blockchains together, allowing for the transfer of assets and data between them. By providing interoperability between blockchains, they can increase overall scalability by distributing transactions across multiple chains.

3. Consensus mechanisms

Developers are exploring innovative consensus mechanisms to improve scalability while maintaining security and decentralization. Examples include:

• Proof of Stake (PoS): PoS replaces the energy-intensive Proof of Work (PoW) with a system where validators are chosen based on the number of coins they hold and are willing to “stake” as collateral. Ethereum 2.0 is transitioning to PoS.
• Delegated Proof of Stake (DPoS): DPoS involves a small number of elected nodes (delegates) responsible for validating transactions and creating blocks. Token holders can vote out underperforming or malicious validators, making DPoS a collaborative consensus mechanism compared to competitive mechanisms like Proof of Work or Proof of Stake. In DPoS, delegates work together to ensure block production. Despite its partial centralization, DPoS blockchain networks typically offer better speed than traditional public blockchain networks.
• Proof-of-Authority (PoA): This consensus mechanism employs a reputation-based algorithm. In PoA, selected nodes are responsible for validating transactions within the network. These nodes function as system administrators, dictating the state of transactions on the blockchain. Participants in a PoA-based blockchain must stake their identities, necessitating a comprehensive and stringent screening process for selecting validators. The identity-based model and higher throughput of PoA make it suitable for private, permissioned blockchain systems.
• Byzantine Fault Tolerance (BFT): BFT consensus mechanisms are trusted solutions for addressing the Byzantine Generals Problem. BFT ensures that a distributed system can achieve consensus consistently, even in the presence of adversarial agents within the network. There are various variants of BFT algorithms that serve as effective solutions for enhancing blockchain scalability.

4. Scalable distributed ledgers

By distributing the workload across multiple nodes in a decentralized manner, scalable distributed ledgers can handle high transaction volumes while maintaining transaction speeds and network capacity.

What is blockchain interoperability?

Blockchain interoperability is the ability of different blockchain networks to communicate, share data, and interact with each other seamlessly. This means that assets, information, and transactions can be transferred across various blockchain platforms without intermediaries.

Interoperability aims to enhance the functionality and utility of blockchain systems by enabling them to work together. This allows for broader adoption and more versatile applications, creating a more interconnected and efficient ecosystem.

Blockchain interoperability is becoming a key requirement as Web3 has developed into a complex landscape, featuring over 100 layer-1 blockchains and a growing number of layer-2 and layer-3 networks. Various blockchains compete by optimizing protocols for specific features, often balancing trade-offs between decentralization, censorship resistance, throughput, and privacy.

Interoperability is crucial for developers creating cross-chain or modularized applications that maintain a unified global state and liquidity across different on-chain environments. It is also essential for application developers who want to utilize the unique assets and features of each blockchain.

How to achieve blockchain interoperability?

Key components of blockchain interoperability include interoperability bridges, cross-chain communication protocols, atomic swaps, and other tools.

Interoperability bridges

Interoperability bridges, such as the Cosmos Network, enable the exchange of information and assets, using the Inter-Blockchain Communication (IBC) protocol. These bridges are instrumental in breaking the isolation of blockchain networks and establishing seamless connections.

Cross-chain communication protocols

Cross-chain communication protocols, like Polkadot’s Relay Chain and Parachain system, are key in standardizing data exchange and transaction verification between blockchain networks. These protocols ensure compatibility and seamless transfer of assets and information.

Atomic swaps

Atomic swaps use smart contracts to ensure both parties receive their assets or the transaction is canceled. For example, a Bitcoin-Ethereum atomic swap involves smart contracts executing the exchange based on predetermined conditions, enhancing security and decentralization.

Sidechains and pegged assets

Sidechains are parallel blockchains facilitating asset transfer alongside the main blockchain, while pegged assets are tokens on one blockchain pegged to equivalent tokens on another. The Liquid Network, a Bitcoin sidechain, allows seamless BTC token transfers, creating interconnected pathways.

Middleware solutions

Middleware solutions act as intermediaries between applications and blockchain networks, providing abstraction layers for translating and relaying information. Chainlink, for example, offers decentralized oracles to securely transmit off-chain data to smart contracts on different blockchains. Middleware solutions enable developers to build decentralized applications interacting with multiple blockchains, enhancing interoperability and expanding blockchain technology’s potential.

Conclusion

Blockchain’s scalability and interoperability are essential for the development of decentralized apps. Scalability solutions enable higher transaction throughput, lower fees, and improved overall performance. The choice depends on the specific goals and needs of the application or network. Layer 1 solutions are most effective for enhancing security and decentralization. Layer 2 solutions can improve performance without compromising security. Consensus mechanisms maximize scalability while maintaining security and decentralization.

Interoperability protocols enable complex applications to function as a unified entity across multiple blockchains and empower organizations to securely access any on-chain environment from a single interface. These advancements are key to developing next-generation nets and dApps.

Originally published at https://serokell.io.

Artificial intelligence is progressing fast. According to Marketsandmarkets, the AI market is expected to surpass $407 billion by 2027, compared to $86.9 billion revenue in 2022. AI is omnipresent and penetrating the areas that were previously considered exclusively human domains such art and creativity, healthcare, and justice.

While some people welcome the widespread adoption of AI and its fast progress, expecting it to resolve global issues, some people are way more skeptical about it. They believe that general artificial intelligence can menace the future of humankind. Therefore, this technology should be developed responsibly.

In this article, we will discuss the difference between these ideologies ― effective accelerators and effective altruists.

What is effective altruism?

Effective altruism (EA) is a philosophical and social movement that is focused on finding the most effective ways to do good for the maximum number of people. In the context of AI, EA advocates for the development and application of AI technologies that maximize positive societal impact while minimizing potential harms and heavily rely on the principles of AI ethics.

Some of the questions this movement is trying to resolve are:

• Who should we care about helping?
• How much better are the best options to do good?
• What can we do to prevent the next pandemic?
• How can we make better decisions together?
• How does climate change compare to other risks?

The movement started to develop at the end of 2000s, led by several evidence-based charity organizations such as GiveWell and Open Philanthropy. Moral philosophers that have been influential to the movements are Peter Singer, Toby Ord, and William MacAskill.

In the field of AI, EA tackles the issue of existential risk from the development of general artificial intelligence. They believe that AI could lead to human extinction or a global catastrophe if not developed responsibly.

This movement adheres to several principles:

1. Prioritizing safety and ethics, even if it slows down AI development. EA emphasizes the importance of developing safe and ethical AI systems. This involves rigorous testing, transparency, and adherence to ethical guidelines to prevent unintended consequences.
2. Focusing on long-term impact. Effective altruists are particularly concerned with the long-term implications of AI. They advocate for research into AI alignment, ensuring that advanced AI systems remain in accord with human values and interests.
3. Using technology for the global benefit. EA encourages the development of AI solutions that address global challenges, such as climate change, poverty, and health crises. By focusing on the broader impact, businesses can contribute to significant positive change.

If you want to learn more about this ideology:

• Doing Good Better by William MacAskill
• The Most Good You Can Do by Peter Singer
• Superintelligence by Nick Bostrom
• Resources on EffectiveaAltruism.org
• World Optimization on LessWrong.com

What is effective accelerationism?

Effective accelerationism (E/Acc), on the other hand, is a philosophy that supports the rapid advancement of technology, including AI. EAcc advocates argue that accelerating technological development can lead to significant benefits, such as economic growth, improved quality of life, and the rapid solving of complex problems.

The movement relies on the theories of Nick Land, an English philosopher. His ideas are an eclectic mix of cybernetics studies, mysticism, speculative realism, and “dark” philosophical interests such as eugenics, anti-egalitarian and anti-democratic ideas. His writings have inspired alternative right and neo-fascist movements.

The founder of effective accelerationism is Guillaume Verdon, a former Google engineer, who sees the rapid development of AI as a way to “usher in the next evolution of consciousness, creating unthinkable next-generation lifeforms.” Other high-profile Silicon Valley figures such as Marc Andressen and Garry Tan. Verdon’s ideas have gained a certain popularity among male software developers in leading tech companies, mostly in the Silicon Valley.

E/accs are less concerned with the ethics of software development or how the uncontrolled development of software systems may harm groups of people that are already marginalized such as women and racial minorities. For example, Business Insider has recently published an article titled “The ‘Effective Accelerationism’ movement doesn’t care if humans are replaced by AI as long as they’re there to make money from it.”

This movement adhere to several principles:

1. Staying optimistic. EAcc is rooted in the belief that technological progress is inherently positive and that accelerating AI development can unlock unprecedented opportunities.
2. Enhancing innovation and competition. Accelerationists advocate for a competitive environment that fosters innovation. They believe that competition drives efficiency and spurs breakthroughs that can benefit society as a whole.
3. Prioritizing economic growth and prosperity. Effective Accelerationism highlights the potential of AI to drive economic growth and prosperity. By rapidly advancing AI technologies, businesses can create new markets, improve productivity, and enhance overall economic well-being.

If you want to learn more about this ideology:

• Schizoid thoughts about effective accelerationism by Beff Jezos
• Accelerate: The Accelerationist Reader by Robin Mackay
• The Thirst for Annihilation by Nick Land
• The Techno-Optimist Manifesto by Marc Andreessen

Balancing opposing ideologies

The question of who is “right” between Effective Altruism (EA) and Effective Accelerationism (EAcc) in the context of AI is complex and doesn’t have a simple answer.

Effective Altruism is centered on the idea of using reason and evidence to maximize positive impact and minimize harm. This approach is particularly valuable when considering the long-term implications of AI, such as ensuring that AI systems are aligned with human values and do not pose existential risks.

In scenarios where the potential risks of AI, such as bias, loss of jobs, or even existential risks from superintelligent AI, are high, EA’s emphasis on thorough risk assessment and alignment is critical.

Effective Accelerationism, by contrast, argues for embracing rapid technological progress, with the belief that accelerated development can lead to breakthroughs that drive economic growth and solve complex problems quickly.

If the primary objective is to drive innovation, create new markets, and boost economic growth, EAcc’s focus on speed and competition may be more aligned with these goals.

In highly competitive industries or nations, rapid advancement in AI might be crucial to maintaining a competitive edge. In such cases, the EAcc approach, which emphasizes moving fast and managing risks proactively, could be more appropriate.

For businesses, it might be about finding a balance between these two philosophies. For example, a company might adopt an accelerationist approach to stay competitive while incorporating altruistic principles to ensure that their innovations do not harm society and are sustainable in the long term.

Read more:

• Security Risks of Generative AI
• A Complete Guide to AGI
• How Does Character.AI Work?

Originally published at https://serokell.io.

The global big data and business analytics market size reached $198.08 billion in 2020 and is expected to triple by 2030. This fast growth shows that it’s crucial for businesses to adopt data analytics strategies to gain advantage in competitive markets.

In this article, we take a look at the data analytics trends in 2024 that you need to incorporate into your business strategy to maintain an edge.

What is data analytics?

Data analytics refers to the process of examining raw data with the purpose of drawing conclusions about that information. It involves various techniques and tools to transform, organize, and model data to discover useful information, support decision-making, and provide actionable insights.

There are various types of data analytics:

• Descriptive analytics. It focuses on summarizing historical data to understand what happened in the past.
• Diagnostic analytics. Looks at past data to determine why something occurred.
• Predictive analytics. Uses historical data and statistical models to predict future outcomes.
• Prescriptive analytics. Recommends actions you can take to affect desired outcomes.

Data analytics is widely used across different industries including business, healthcare, marketing, and finance to improve operational efficiency and guide strategic decisions.

How has the data analytics landscape changed in recent years?

Lately, the market of data analytics has changed significantly. These are some of the contributing factors:

LLMs emergence

In recent years, the data analytics market has undergone significant changes, due to the emergence and strengthening of large language models (LLMs). Previously, data analytics relied on concrete and formal sources such as financial reports and stock exchange data. Today, technology enables effective analysis of more abstract and diverse sources, including meeting transcripts, news, and various public documents. These data have become more accessible due to transparency and anti-corruption laws, which require open publication of information.

Increased data availability

The rise of social media and mobile technology has resulted in vast amounts of user-generated data. In fact, the total amount of data created, captured, copied, and consumed globally is forecasted to grow to more than 180 zettabytes. The growth was accelerated by the pandemic, with more people working and learning from home.

However, alongside this, a new problem emerged: potential inaccuracies and unreliability of information. AI is actively used in various sources, which can lead to data theft and errors. While analysts mainly relied on official data in the past, they now need to learn to work with potentially unreliable information.

Additional regulations

An additional factor is the new regulations regarding personal data. For example, the European Union has introduced laws concerning cookies and restrictions on what data can be used for model training. When analyzing data such as tweets, it is important to consider in which jurisdiction they were collected and how lawful their use is.

In the future, it is likely that regulations that concern private data and data used to train generational artificial intelligence will become even more strict, especially in Europe, where there is already an existent trend.

Cultural shift

Many organizations now embrace a data-driven culture, where data is central to decision-making processes. This cultural shift has been driven by the recognition of data as a critical asset. Data preservation and validation have become crucial aspects. Companies now pay more attention to the quality of their datasets, striving to avoid generated data. For improving analytics quality, it is preferable to have fewer but more reliable records. This requires a meticulous approach to data collection and improving metrics by enhancing data quality.

Moreover, over the past three years, companies have realized the importance of data, leading to an increase in its value and complexity of access. Data that was previously considered publicly available now often requires purchasing rights for its use. This is especially noticeable on platforms like Twitter, where collecting data without permission can lead to bans and legal consequences.

The market also sees a growing demand for unique datasets, especially for rare languages or niche industries. Previously, there were companies providing such data in large quantities, but today, while data is available, the opportunities for analysis are becoming fewer. Companies prefer to avoid sharing data indiscriminately and focus on analyzing it more selectively.

Finally, a significant trend has been the focus on diversity and inclusivity in data. Companies aim to collect data that reflects diversity and ensures inclusivity, which is important for creating more accurate and representative models.

Top data analytics trends

Organizations that leverage these trends are better positioned to gain competitive advantages and make informed decisions.

1. Edge analytics

The ARM architecture has a long history, with principles dating back 100 years and initially used in bionics. Today, this technology is advancing rapidly in computing devices. The popularity of ARM-based devices like smartwatches and smartphones is growing, and the possibility of ARM-based servers and computers is on the horizon. This evolution means that software developers may no longer need to rewrite and recompile software for different processor architectures, significantly reducing the complexity and workload.

With the widespread adoption of ARM architecture, coding becomes simpler and more efficient. A game or application developed for one ARM-based device can run seamlessly across various devices, from mobile phones to servers, ensuring a consistent and efficient user experience. The reduction in energy consumption and increased efficiency of ARM chips also contribute to the sustainability of computing technologies.

In addition to hardware advancements, the development of compact large language models (LLMs) has further enhanced edge analytics. Models like LLaMA can run on mobile phones, allowing for sophisticated data processing directly on the device. This capability reduces the need to collect and process vast amounts of data on centralized servers, shifting the focus to data processing at the edge.

Gartner predicts that more than 50% of critical data will be created and processed outside of the enterprise’s data center and cloud by 2025.

2. Data democratization

By leveraging techniques such as Retrieval-Augmented Generation (RAG) and knowledge maps, companies can create intelligent systems that provide employees with precise answers and easy access to necessary documents and data.

Training chatbots on internal data involves creating a system that understands the structure and location of documents within the company. This process includes aggregating various types of documents and data sources across the organization, as well as allowing the chatbot to fetch relevant documents and information from a vast database in response to specific queries.

Developing a knowledge map helps in structuring and indexing the data, making it easier for the chatbot to locate and provide the right information. Knowledge maps visually represent the relationships between different pieces of information, guiding the chatbot in understanding the context and relevance of the data.

One of the major challenges in deploying such chatbots is ensuring proper access control to sensitive information. For example, by implementing RBAC (role-based access control) that ensures employees only have access to data relevant to their roles. The chatbot needs to be integrated with the company’s identity and access management (IAM) system to enforce these controls.

A Harvard Business Review survey found that 97% of business leaders report that democratizing data is crucial to the success of their business. But only 60% of them say their organizations are effective at granting employees access to data and the tools they need to analyze it.

3. Augmented analytics

LLMs have revolutionized how we process and interpret vast amounts of information. With their advanced natural language understanding, these models can effectively analyze news articles, identifying key trends, sentiments, and insights that are valuable for businesses and researchers.

LLMs serve as expert tools in extracting and annotating visual data. This process, which traditionally required human intervention or external services, has become more automated and efficient. By employing LLMs, organizations can generate annotated datasets quickly, facilitating machine learning model training and other analytical tasks.

The global augmented analytics market has witnessed significant growth, valued at $8.95 billion in 2023. Experts expect it to reach $11.66 billion in 2024 and surge to $91.46 billion by 2032.

4. Natural language processing (NLP)

In 2024, developing proprietary NLP models has become less common as more organizations prefer to leverage existing models via APIs. Building and maintaining custom NLP models is often seen as unnecessary, except for cases where there is a need to keep data internal due to privacy or security concerns.

Many complex tasks that were previously challenging, such as reference resolution and database matching, have become significantly easier with advanced models like ChatGPT. For example, merging databases from different clients with varying structures and field names is a task that traditional NLP struggled with. However, ChatGPT and similar models can handle these tasks more effectively, offering more accurate and efficient solutions.

These advancements are primarily beneficial for high-resource languages, where large models perform exceptionally well. However, their effectiveness diminishes for languages with fewer resources and less available training data. Moreover, while LLMs excel in many areas, code generation is still an emerging capability and currently performs less reliably. This remains an area of ongoing improvement.

The market size in the Natural Language Processing market is projected to reach US$36 billion in 2024. It is expected to show an annual growth rate of 27.55%, resulting in a market volume of US$156.80bn by 2030.

5. Data fabric

Data fabric is an emerging architectural approach that enables seamless data integration and management across diverse data environments. It provides a unified view of data by connecting disparate data sources, whether on-premises or in the cloud.

Previously, multimodal models required training multiple models for various types of data, such as graphics, sound, and text, and then combining their outputs. Now, the approach has shifted to integrating all these data types into a single, large contextual vector, which can then be processed collectively. This unified method allows for more efficient handling of diverse data sources. This technique not only simplifies the model architecture but also enhances the performance and accuracy of the analysis.

By the end of 2024, 25% of data management suppliers, up from 5% currently, will offer a complete foundation for data fabric.

6. Graph analytics

Graph analytics focuses on relationships between data points, making it particularly useful for analyzing complex networks such as social media connections, supply chains, and fraud detection.

Traditionally, graphs are defined using either adjacency matrices or adjacency lists (edge lists). Modern approaches involve converting these graph structures into vector representations. This process, often referred to as “flattening” the graph, enables neural networks to analyze the data more effectively.

Transformer-based models have become integral to this process. By encoding graph data into vector formats, transformers can process and interpret complex relationships within the graph. This method allows for sophisticated analysis and provides insightful answers to queries about the graph data.

7. Computer vision

Two key technologies driving changes in this field are transformers and diffusion models, which have revolutionized how data is generated and analyzed.

Transformers, originally designed for natural language processing, have found significant applications in computer vision. These models excel at capturing long-range dependencies and contextual relationships within data, making them ideal for complex image analysis tasks such as image classification, image segmentation, and object recognition.

Diffusion models have emerged as powerful tools for data generation in computer vision. These models work by iteratively refining random noise into coherent images, allowing for the creation of highly realistic and diverse datasets. They are used for creating synthetic data: large, high-quality datasets for training computer vision models. This is particularly useful in scenarios where collecting real-world data is challenging or expensive.

Moreover, these models enable the generation of images in various artistic styles, facilitating creative applications in design and media.

Conclusion

The landscape of data analytics is rapidly evolving, driven by advancements in technology and changing business needs. Staying abreast of these top trends is essential for organizations to enhance their efficiency and maintain a competitive edge in the market.

Read more:

• AI Integration in Oil and Gas
• Software Development Project Management
• How AI Is Reshaping Manufacturing

Originally published at https://serokell.io.

Artificial intelligence and blockchain are two of the most transformative technologies of our time. When integrated, they open up a whole range of new possibilities underpinned by AI’s significant productivity enhancements and blockchain’s security and transparency.

In this post, we explore the integration of AI in blockchain and the benefits of merging the two.

How can blockchain improve AI?

The problem with artificial intelligence is that its decision-making process lacks transparency due to complicated multi-parameter calculations, and extensive data arrays. Advanced machine learning models, such as deep neural networks, often operate as black boxes. This issue, often referred to as “explainability,” raises concerns about trust and AI ethics.

Blockchain’s key features, such as immutable and transparent digital records and decentralized data storage, can offer valuable insights into the typically centralized and opaque nature of AI. By integrating blockchain, AI systems could benefit from enhanced trust, privacy, and accountability.

Here’s a closer look at how this could work:

Trust

Blockchain provides a tamper-proof ledger where each transaction is permanently recorded. This can be used to store and track every decision made by an AI system, along with the data it was based on. This immutable audit trail ensures that any changes or inputs are transparent and traceable. This approach allows scientists to analyze each step of the calculations involved in any ML algorithm, including tracing through the layers of deep neural networks, to better understand how conclusions are drawn.

With blockchain, all network participants have access to the same information, which can lead to increased transparency. This means that the operational and decision-making processes of AI systems can be accessible to all relevant stakeholders, which is crucial for developing trust.

Data security and integrity

Blockchain’s decentralized nodes can serve as cryptographically protected data storages for artificial intelligence algorithms. Direct, verified access to databases on the blockchain by AI models ensures that confidential information is neither disclosed nor altered, as it bypasses any intermediary handlers.

Computing power

AI is resource-intensive and requires substantial computational power. For the most complex tasks, we need to upgrade centralized data servers, and memory storage hardware. Blockchain can help by sustainably distributing the computing workload across multiple machines.

How can AI enhance blockchain?

While blockchain technology can enhance the interpretability of AI, artificial intelligence offers tools for identifying and analyzing connections within blockchain.

Moreover, AI algorithms can help detect fraudulent activity on blockchains. Below, we take a closer look at the benefits of integration AI in blockchain.

Better data management

AI can analyze patterns and optimize the hashing process, streamlining the data management process. By employing machine learning models, AI can predict the most likely successful hash combinations based on historical data and current network conditions.

Optimized energy consumption

In blockchain technology, data mining involves validating transactions and adding them to the blockchain ledger. This process is computationally intensive and requires significant energy.

ML algorithms can analyze and streamline these processes. By identifying inefficiencies and optimizing the operations, AI can reduce the amount of computational power required.

Improved scalability

AI addresses the blockchain scaling challenge by introducing advanced decentralized machine learning systems and innovative data-sharing techniques. This not only improves efficiency but also creates opportunities for startups and enterprises within the blockchain ecosystem.

Transaction efficiency

AI algorithms can optimize the way transactions are processed on the blockchain by predicting peak times and distributing the load more evenly, reducing bottlenecks and ensuring faster transactions. AI can also improve the efficiency of smart contract execution by predicting potential issues and optimizing contract code.

Augmented security

While blockchain is known for its strong security features, applications built with this technology are not totally immune to flaws. AI integration can provide additional automated testing and real-time data transformation capabilities to blockchain’s peer-to-peer linking. This combination allows blockchain developers to securely optimize processes.

Innovative data management

In the future, all data is expected to be stored on a blockchain. This means organizations will be able to purchase data directly from holders. Acting as a data gatekeeper, AI will ensure that the flow of blockchain data is streamlined.

AI and blockchain integration: areas of application

The integration of AI and blockchain can effectively address concerns about data security and workflow optimization. This combination allows for the seamless integration of research processes and results into immutable ledgers. Additionally, blockchain technology and smart contracts enhance data security and integrity for AI.

Below are some practical examples of blockchain and artificial intelligence conjunction.

Healthcare

Together, AI and blockchain can improve data management and privacy protection in healthcare by securely storing and sharing patient records and medical test data. Techniques like homomorphic encryption allow computations on this data without compromising privacy.

This facilitates collaboration among healthcare researchers across different locations while maintaining high data security standards.

Retail

Merging AI with blockchain enables retailers to save customer insights in immutable blocks, record entire processes, and analyze factors contributing to the success or failure of marketing plans. Additionally, it enhances the payment process and reduces the risk of fraud.

Supply chain

The integration of two technologies can improve transparency, reduce fraud, and enable real-time tracking of goods from production to end users. AI models can use predetermined conditions within smart contracts to automate tasks, such as detecting inventory needs and placing orders with suppliers.

Data analytics

Blockchain excels in providing data provenance and ensuring long-term data integrity through its secure, decentralized networks, making it ideal for large-scale data analytics. As blockchain becomes integral to economic and social activities, sophisticated machine learning models can analyze on-chain data fast and securely.

Cybersecurity

Decentralized infrastructure and blockchain technology can serve as encryption-backed safeguards for AI systems, limiting misuse and adversarial behaviors.

Financial services

Large language models can utilize the on-chain financial stack of the Web3 industry to perform routine payment or economic exchange tasks. The composability of blockchain applications allows AI models to handle complex financial transactions without intermediaries.

AI-driven automated investment strategies in DeFi can create new financial services supported by secure, transparent, and decentralized infrastructure.

Government

The merging of blockchain and AI can help transfer control over data from centralized entities to the public while maintaining data security and quality. These technologies will help trace e-voting procedures, making them accessible to all citizens in real-time.

Smart contract development

In the future, smart contracts could be created using natural language and prompts, instead of programming languages, and then converted into code with the assistance of ML models. Validators would reach consensus on the correct output, which would be executed by the blockchain network. AI-powered APIs can also enhance smart contract applications with real-world analytics, sentiment analysis, and generative models, ushering in a new generation of Web3 applications.

Media

As deep learning models like DALL-E, Stable Diffusion, and Midjourney gain momentum, the risk of using them for generating misinformation and deep fakes increases. Blockchain’s cryptographic watermarking and tamper-proof timestamping can authenticate content and ensure it hasn’t been altered.

Additionally, non-fungible tokens (NFTs) can address challenges in verifying the authenticity and provenance of digital content. By assigning an NFT to a piece of content, creators can set a digital fingerprint, making the content’s origin, ownership history, and modifications transparent and verifiable.

Use cases for integrated AI and blockchain

Having explored the various use cases of blockchain and AI, let’s now examine the platforms that are already leveraging the combined power of these technologies.

SingularityNET

SingularityNET is the leading decentralized AI marketplace that utilizes blockchain. Its primary mission is to develop Artificial General Intelligence (AGI) and decentralize AI, promoting a fair distribution of power, value, and technology worldwide.

The SingularityNET marketplace allows users to search, trial, and choose from a continually expanding library of AI algorithms. The platform’s publishing infrastructure serves as a central hub for creating, editing, and managing AI solutions, providing organizations with the necessary tools to launch services globally.

NEAR protocol

Initially an AI-focused company founded by former Google and Microsoft engineers in 2017, NEAR pivoted to blockchain in 2018. NEAR is designed to address Ethereum’s limitations through novel sharding technology, Nightshade, enabling faster, more efficient transactions. It also focuses on user-friendly features and developer incentives. NEAR protocol is actively exploring the integration of AI into its ecosystem through decentralized AI training methods, allowing contributions from a blockchain-based community.

DeepBrain Chain

DeepBrain Chain is a blockchain-driven platform that provides a decentralized, high-performance GPU computing network. Their goal is to become the most widely used GPU computing infrastructure globally in the AI and Metaverse era.

DeepBrain Chain’s mission is to accelerate the advancement of artificial intelligence in an era that is undergoing an explosion of smart devices and their computational needs. Founded by AI veterans, DBC uses blockchain technology to develop a distributed, low-cost and privacy-protecting AI computing platform that aims to address the pain points of the industry back. — Source

Blackbird.AI

Blackbird.AI combines AI and blockchain to analyze news and information’s provenance and authenticity. They have developed an AI-based Narrative Intelligence Platform called Constellation, which automatically detects, analyzes, and measures risks associated with harmful narratives created by misinformation.

Matrix

Matrix is a blockchain platform powered by AI that can be applied in multiple sectors. Some of the features it offers include:

• Creating personalized AI-powered digital avatars secured on blockchain.
• Building AI-driven blockchain applications with no-code tools.
• Decentralizing machine learning processes through blockchain (distributed ML).
• Enhancing brain-computer interfaces and cognitive computing capabilities in neuroscience research.

Conclusion

The potential for transforming various sectors through AI and blockchain is tremendous. According to Precedence Research, the global blockchain AI market size, valued at USD 445.41 million in 2023, rose to USD 550.70 million in 2024. It is projected to reach approximately USD 3,718.34 million by 2033.

As companies strive to automate tasks, boost productivity, and enhance their business offerings, AI models are expected to increasingly permeate various segments of the economy.

At the same time, as trust in institutions declines, users gravitate toward applications offering cryptographic guarantees. This convergence of AI and blockchain technology is poised to fundamentally reshape our life.

Originally published at https://serokell.io.

Machine learning professionals are always on the lookout for diverse datasets to develop innovative and powerful models. In this blog post, we have compiled 25 free datasets, categorized by industry, to help you get started.

Healthcare

In this section, you will find 5 free datasets for the healthcare and medicine industries.

1. Breast Cancer Wisconsin Dataset

This dataset contains features of cell nuclei derived from breast cancer biopsy images. Each instance is described by 30 attributes such as radius, texture, and perimeter. The data is used to classify tumors as either benign or malignant. It’s widely utilized for building diagnostic models to assist in early detection and treatment planning for breast cancer.

2. MIMIC-III clinical database

MIMIC-III is a comprehensive database containing de-identified health data from approximately 60,000 intensive care unit (ICU) patients. It includes demographics, vital signs, laboratory tests, medications, and notes from healthcare providers. Researchers use this dataset to develop predictive models for patient outcomes, understand disease progression, and improve ICU management. The richness of the data supports diverse research initiatives in clinical medicine and healthcare.

3. COVID-19 Open Research Dataset (CORD-19)

CORD-19 is a growing resource of scholarly articles related to Covid-19 and other coronaviruses. It includes titles, abstracts, full-text articles, and relevant metadata. Researchers leverage this dataset for text mining, natural language processing, and machine learning tasks to extract insights and accelerate the development of treatments and vaccines. It plays a crucial role in understanding the virus’s characteristics, transmission patterns, and impacts.

4. Diabetes dataset

This dataset includes 20 diagnostic measurements of diabetes such as glucose levels, blood pressure, skin thickness, insulin levels, and BMI. It is often used for binary classification to predict whether a patient has diabetes based on these medical attributes. Researchers and data scientists employ this dataset to develop predictive models and improve screening processes for diabetes management and prevention.

5. Human Activity Recognition Using Smartphones dataset

The dataset captures various physical activities (like walking, sitting, standing) through accelerometer and gyroscope data recorded from smartphones. Each instance is a multivariate time series with sensor data labeled according to the activity performed. It is commonly used for building models that can recognize and classify human activities, aiding in applications such as health monitoring, fitness tracking, and personalized healthcare solutions.

Finance

In the finance section, you will find data to build models for market fluctuations and fraud detection predictions.

1. Yahoo Finance stock market data

This dataset includes historical stock prices, trading volumes, and other financial information for a wide range of publicly traded companies. The data can be used to analyze market trends, perform technical analysis, and develop predictive models for stock price movements. Researchers and financial analysts leverage this dataset to test investment strategies, conduct financial forecasting, and study the behavior of financial markets.

2. Lending Club loan data

This dataset contains detailed information about loans issued through the Lending Club platform, including loan amount, borrower demographics, credit scores, payment history, and loan status. It is primarily used for credit risk modeling and default prediction. Financial analysts and data scientists use this data to build models that can assess the risk of new loan applications and improve lending decisions.

3. Cryptocurrency historical prices

The dataset comprises historical price data for various cryptocurrencies, including daily prices, market capitalization, and trading volumes. It is useful for analyzing the volatile cryptocurrency market, identifying trading opportunities, and developing automated trading strategies. Researchers use this data to study price dynamics, market correlations, and the impact of external events on cryptocurrency values.

4. Credit card fraud detection dataset

This dataset includes anonymized credit card transactions, with each transaction labeled as fraudulent or legitimate. It contains various features such as transaction amount, time, and derived attributes from the original features. Data scientists use this dataset to develop and evaluate models for fraud detection, aiming to minimize false positives and accurately identify fraudulent activities.

5. Financial news articles dataset

This dataset consists of financial news articles and metadata, often including sentiment labels or scores. It is used for sentiment analysis and its impact on stock prices and market movements. Researchers analyze the text data to build models that can predict market reactions based on news sentiment, enhance trading algorithms, and study the relationship between media coverage and financial markets.

Marketing

Marketing datasets are useful for customer segmentation, sentiment analysis, and price strategy analysis.

1. Online retail dataset

This dataset contains transactional data from a UK-based online retail store, including details about invoices, stock codes, product descriptions, quantities, and customer information. It is used for analyzing purchasing patterns, customer segmentation, and sales forecasting. Data scientists and marketers use this data to develop strategies for inventory management, customer retention, and targeted marketing campaigns.

2. Google Merchandise Store analytics dataset

The dataset includes Google Analytics data from the Google Merchandise Store, featuring information on user behavior, traffic sources, session durations, and e-commerce metrics. It is useful for understanding user engagement, optimizing website performance, and improving conversion rates. Marketers use this data to analyze the effectiveness of marketing channels, design better user experiences, and enhance digital marketing strategies.

3. Customer segmentation dataset

This dataset contains customer demographics, purchasing behavior, and transaction history, typically from a retail or e-commerce platform. It is used for clustering customers into distinct segments based on their behavior and preferences. Marketers and data analysts use this data to tailor marketing campaigns, personalize customer experiences, and improve customer relationship management.

4. Black Friday dataset

The Black Friday dataset includes sales transaction data from Black Friday, featuring customer demographics, product details, and purchase amounts. It is used to analyze consumer behavior during peak shopping periods, identify popular products, and understand spending patterns. Marketers utilize this data to plan promotional strategies, optimize pricing, and enhance inventory management during high-demand events.

5. Email marketing Dataset

This dataset comprises data from email marketing campaigns, including metrics such as open rates, click-through and conversion rates. It is used to analyze the effectiveness of email campaigns, segment audiences, and improve engagement strategies. Marketers employ this data to test different email formats, optimize content, and increase the overall effectiveness of their email marketing efforts.

E-commerce

In this section, you will find 5 datasets for retail and e-commerce industries.

1. Amazon product reviews

This dataset includes customer reviews for Amazon products, featuring review text, star ratings, and metadata such as product IDs and review timestamps. It is used for sentiment analysis, product recommendation systems, and understanding customer feedback. Data scientists and marketers analyze this data to improve product descriptions, enhance customer satisfaction, and develop targeted marketing strategies.

2. eBay online auction dataset

The dataset consists of data from online auctions conducted on eBay, including item descriptions, starting bids, final prices, bid history, and auction duration. It is utilized to analyze bidding behavior, predict auction outcomes, and optimize auction strategies. Researchers and analysts use this data to study market dynamics, enhance auction designs, and understand the factors influencing bidding patterns.

3. UCI e-commerce dataset

This dataset includes customer behavior data from a Brazilian e-commerce platform, featuring user sessions, page views, product categories, and purchase details. It is used to analyze browsing patterns, predict purchase intent, and optimize website design. E-commerce analysts use this data to improve user experience, increase conversion rates, and design personalized marketing campaigns.

4. Instacart market basket analysis

The Instacart dataset contains data on orders from Instacart, an online grocery delivery service, including order details, product information, and user behavior. It is used for market basket analysis, recommendation systems, and understanding customer purchasing habits. Data scientists leverage this data to develop models for product recommendations, optimize inventory, and design targeted promotions.

5. Flipkart product listings dataset

This dataset includes product listings from Flipkart, an Indian e-commerce website, featuring product names, categories, prices, and descriptions. It is used to analyze product trends, optimize search algorithms, and improve product recommendations. E-commerce professionals use this data to enhance product visibility, understand market demand, and develop strategies for competitive pricing and promotions.

Natural language processing (NLP)

In this section, you will find datasets that can be used for NLP tasks across various industries.

1. IMDB movie reviews dataset

This dataset contains 50,000 movie reviews from IMDB, labeled as positive or negative. It is widely used for sentiment analysis tasks to classify reviews based on their sentiment. Researchers and data scientists utilize this dataset to train models that can understand and interpret the sentiment expressed in textual data, helpful in applications like opinion mining and recommendation systems.

2. 20 Newsgroups dataset

The 20 Newsgroups dataset comprises approximately 20,000 newsgroup documents across 20 different newsgroups. It is used for text classification, topic modeling, and natural language processing tasks. This dataset helps in developing models that can classify text into categories, perform topic extraction, and analyze trends in textual content from different domains.

3. Quora question pairs dataset

This dataset includes pairs of questions from Quora, labeled to indicate if they are duplicate (i.e., if they have the same intent). It is used to build models for detecting duplicate questions, improving question-answering systems, and enhancing community question-answer platforms. Data scientists employ this dataset to develop techniques for semantic similarity, text matching, and clustering of questions.

4. Wikipedia text dataset

The Wikipedia text dataset contains a large corpus of textual data extracted from Wikipedia articles. It is used for various NLP tasks such as language modeling, text generation, and information retrieval. Researchers leverage this dataset to train language models, develop summarization algorithms, and enhance knowledge extraction from vast textual sources.

5. Twitter sentiment analysis dataset

This dataset consists of tweets labeled with sentiment scores (positive, negative, or neutral). It is used for sentiment analysis and opinion mining on social media data. Data scientists use this dataset to build models that can analyze public sentiment, track trends in social media conversations, and understand user opinions on various topics and events.

Image recognition

In this section, you will find five helpful datasets for developing and evaluating models for image recognition tasks.

1. CIFAR-10 dataset

The CIFAR-10 dataset consists of 60,000 32x32 color images categorized into 10 classes, such as airplanes, cars, birds, cats, and deer. It is widely used for benchmarking image classification algorithms. Researchers and data scientists utilize this dataset to develop and evaluate models for image recognition tasks, fostering advancements in computer vision techniques and deep learning architectures.

2. MNIST handwritten digits dataset

The MNIST dataset contains 70,000 grayscale images of handwritten digits (0–9), each of size 28x28 pixels. It is primarily used for training and testing image processing systems in digit classification. This dataset is a standard benchmark for evaluating machine learning algorithms and is instrumental in developing techniques for optical character recognition (OCR).

3. Fashion MNIST

Fashion MNIST is a dataset of 70,000 grayscale images of fashion products, each 28x28 pixels, categorized into 10 classes such as t-shirts, trousers, and shoes. It serves as a drop-in replacement for the original MNIST dataset but with more complex and varied visual features. Researchers use this dataset to test image classification models and improve systems for fashion product recognition and categorization.

4. ImageNet dataset

The ImageNet dataset contains over 14 million images organized according to the WordNet hierarchy, with each image labeled by human annotators. It includes more than 20,000 categories and is used for large-scale image classification, object detection, and image segmentation tasks. This dataset is a crucial resource for training deep learning models and has been foundational in advancing the field of computer vision.

5. COCO Dataset

The COCO (Common Objects in Context) dataset comprises over 330,000 images, with more than 200,000 labeled instances across 80 object categories. It includes annotations for object detection, segmentation, and image captioning tasks. Researchers use this dataset to develop and benchmark models for object recognition, instance segmentation, and contextual understanding in images, contributing significantly to advancements in visual perception technologies.

Conclusion

By exploring and utilizing these datasets, machine learning professionals can broaden their expertise and work on a wide range of problems across various industries. Whether you are working on healthcare diagnostics, financial forecasting, marketing strategies, e-commerce analytics, natural language processing, or image recognition, these datasets provide a robust foundation for developing cutting-edge models and solutions.

Read more:

• Where to Find the Best Machine Learning Datasets
• A Beginner’s Guide to Data Mining Techniques
• What Is Data Preprocessing in ML?

Originally published at https://serokell.io.

25 Free Datasets for ML Pros Across Industries was originally published in Geek Culture on Medium, where people are continuing the conversation by highlighting and responding to this story.

Clustering in machine learning refers to the task of grouping similar data points together based on certain characteristics. It allows us to facilitate insights and decision-making when large amounts of data are involved.

In this article, we explore the fundamental concepts of clustering algorithms, their various types, pros and cons, and the scenarios in which each works best.

What is clustering?

Clustering is a fundamental technique in unsupervised machine learning, where the goal is to group similar data points together based on certain features or characteristics. The objective of clustering is to identify inherent structures within a dataset without any prior knowledge of labels or categories.

For example, imagine that you’re a data scientist working at a major supermarket chain. To increase sales, you need to provide different kinds of customers with a personalized offer. Cluster analysis will help with customer segmentation. When you start performing the task you don’t know how many different groups, or clusters of customers, you may have.

Clustering algorithms will discover patterns in data. For instance:

• Parents that often buy childcare products.
• Students that only shop in the discounted section.
• Young professionals that are main consumers of ready-made foods and snacks.
• Families that shop big once a week.

Once you have these clusters, you can offer each a personalized discount.

In clustering, data points that are similar to each other are grouped into the same cluster, while those that are dissimilar are placed in different clusters. The similarity between data points is typically measured using distance metrics, such as Euclidean distance or cosine similarity, depending on the nature of the data.

There are various algorithms used for clustering. Some common clustering algorithms include K-means, hierarchical clustering, DBSCAN, and Gaussian mixture models.

Clustering finds applications in various fields such as customer segmentation, anomaly detection, document clustering, image segmentation, and more.

What are clustering algorithms?

Let’s explore some prominent types:

Partitioning methods

Partitioning methods divide data into non-overlapping clusters, with each data point belonging to exactly one cluster. Notable examples include K-Means and K-Medoids algorithms. While K-Means is efficient and straightforward to implement, K-Medoids offers robustness against outliers due to its use of representative points (medoids) within clusters.

Hierarchical methods

Hierarchical clustering builds a tree-like hierarchy of clusters, either from the bottom up (agglomerative) or top down (divisive). Agglomerative methods iteratively merge similar clusters until a stopping criterion is met, whereas divisive methods recursively split clusters into smaller ones. These methods provide valuable insights into hierarchical structures present in the data, but they can be computationally intensive.

Density-based methods

Density-based clustering, exemplified by algorithms like DBSCAN and OPTICS, groups together points based on their density within the dataset. DBSCAN, for instance, identifies dense regions separated by areas of lower density, making it robust to outliers and capable of detecting clusters of arbitrary shapes.

Distribution-based methods

Distribution-based clustering assumes that data points are generated from a mixture of probability distributions. Gaussian Mixture Models (GMM) is a classic example, where clusters are modeled as Gaussian distributions. GMM is effective for capturing complex data distributions but may struggle with high-dimensional data and requires careful initialization.

Spectral clustering

Spectral clustering treats data points as nodes in a graph and leverages spectral graph theory to partition them into clusters. This method is particularly useful for datasets with nonlinear boundaries and can handle high-dimensional data efficiently. However, it may not scale well to large datasets.

Each type of clustering algorithm comes with its own set of advantages and limitations:

• Partitioning methods like K-Means are scalable and easy to interpret but require specifying the number of clusters beforehand.
• Hierarchical methods offer insights into hierarchical structures but can be computationally expensive.
• Density-based methods are robust to outliers but struggle with varying density levels.
• Distribution-based methods capture complex data distributions but may be sensitive to initialization.
• Spectral clustering handles nonlinear data well but may not scale to large datasets.

Popular clustering algorithms

Clustering algorithms are essential tools in the data scientist’s arsenal, providing insights into the underlying structure of data.

K-Means clustering

K-Means clustering is a widely-used partitioning method that aims to partition data points into K clusters. The algorithm iteratively assigns each data point to the nearest cluster centroid and updates the centroids based on the mean of the data points assigned to each cluster.

The choice of K, the number of clusters, is crucial and often determined using techniques like the elbow method or silhouette score. K-Means finds applications in various domains, including image segmentation, document clustering, and customer segmentation. However, it struggles with clusters of different sizes and shapes and requires specifying the number of clusters beforehand.

Hierarchical сlustering

Hierarchical clustering builds a tree-like hierarchy of clusters, offering insights into the hierarchical structure of data. Two main methods, agglomerative and divisive, are commonly used:

• Agglomerative methods start with each data point as a singleton cluster and iteratively merge the closest clusters until a single cluster remains.
• Divisive methods start with the entire dataset as one cluster and recursively split it into smaller clusters.

Dendrogram visualization is often used to represent the hierarchical relationships between clusters. While hierarchical clustering provides valuable insights into the data structure, it can be computationally expensive, especially for large datasets.

Density-based clustering (DBSCAN)

Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a density-based clustering algorithm that groups together data points based on their density within the dataset.

It requires two parameters:

• epsilon (ε), the radius within which to search for neighboring points,
• minPts, the minimum number of points required to form a dense region.

DBSCAN is robust to outliers and capable of detecting clusters of arbitrary shapes, making it suitable for datasets with varying density levels. It finds applications in anomaly detection, spatial data analysis, and more.

Gaussian Mixture Models (GMM)

Gaussian Mixture Models (GMM) assume that data points are generated from a mixture of several Gaussian distributions. The algorithm iteratively estimates the parameters of these Gaussian distributions, including the mean and covariance matrix, to maximize the likelihood of observing the data.

GMM is effective for capturing complex data distributions and finding clusters with overlapping boundaries. However, it may struggle with high-dimensional data and requires careful initialization.

Spectral clustering

Spectral clustering treats data points as nodes in a graph and leverages the eigenvectors of the graph Laplacian to partition them into clusters. It offers insights into the underlying geometric structure of the data and can handle nonlinear boundaries efficiently.

Spectral clustering finds applications in image segmentation, community detection, and more.

Practical applications

Clustering algorithms are powerful tools with a wide range of practical applications across diverse domains.

Customer segmentation in marketing

By grouping customers with similar characteristics together, businesses can tailor their marketing strategies to specific segments, leading to more effective campaigns and higher customer satisfaction.

For instance, in the example above, a retail company may use clustering to identify different customer segments based on demographics, purchasing behavior, and preferences. This segmentation can then inform targeted marketing initiatives, personalized recommendations, and product development strategies.

Image segmentation in computer vision

In the field of computer vision, image segmentation plays a crucial role in tasks such as object detection, image understanding, and medical imaging. Clustering algorithms are often employed to partition images into meaningful regions or objects based on similarities in color, texture, or intensity. This enables computers to analyze and interpret visual data more effectively.

For example, in autonomous driving systems, clustering algorithms can segment images captured by cameras into distinct objects like cars, pedestrians, and road signs, facilitating real-time decision-making and navigation.

Anomaly detection in cybersecurity

Detecting anomalies in large-scale networks is a critical challenge in cybersecurity. Clustering algorithms offer an effective approach to identifying unusual patterns or behaviors that deviate from normal network activity. By clustering network traffic data, anomalies such as intrusion attempts, malware infections, and data breaches can be detected and mitigated in a timely manner.

For instance, anomaly detection systems powered by clustering algorithms can flag unusual patterns in user access logs, network traffic, and system behavior, enabling cybersecurity professionals to respond swiftly to potential threats and vulnerabilities.

Document clustering in natural language processing

In the realm of natural language processing (NLP), clustering algorithms are used to organize large collections of text documents into coherent groups based on semantic similarities. This enables tasks such as document categorization, topic modeling, and sentiment analysis.

For example, news aggregators may employ clustering algorithms to categorize articles into topics such as politics, sports, and entertainment, allowing users to discover relevant content more efficiently. Similarly, clustering can aid in organizing and summarizing research papers, customer reviews, and social media posts, facilitating knowledge discovery and decision-making.

Best practices and tips

In this section, we will share some of the best practices that will help you to achieve the best results with clustering algorithms.

Preprocessing data

Before applying clustering algorithms, it’s essential to preprocess the data to ensure its quality and suitability for clustering. Some key preprocessing steps include:

1. Data cleaning. Remove any irrelevant or redundant features, handle missing values, and address inconsistencies in the data.
2. Normalization or standardization. Scale the features to a common range to prevent biases towards certain features and ensure equal contribution from all variables.
3. Dimensionality reduction. Reduce the dimensionality of the data if it’s high-dimensional, using techniques like Principal Component Analysis (PCA) or feature selection methods. This can help improve the clustering performance and reduce computational complexity.
4. Outlier detection and treatment. Identify and handle outliers appropriately, as they can significantly impact the clustering results. Consider techniques such as outlier removal or transformation.

Evaluation metrics for assessing clustering performance

Evaluating the performance of clustering algorithms is crucial for assessing their effectiveness and selecting the most suitable approach. Some commonly used evaluation metrics include:

1. Silhouette Score. Measures the cohesion and separation of clusters, ranging from -1 to 1, with higher values indicating better clustering. It considers both intra-cluster similarity and inter-cluster dissimilarity.
2. Davies-Bouldin Index. Quantifies the average similarity between each cluster and its most similar cluster, with lower values indicating better clustering. It evaluates the compactness and separation of clusters.
3. Adjusted Rand Index (ARI). Compares the clustering results to ground truth labels, providing a measure of similarity between the true labels and the clustering output. It ranges from -1 to 1, with higher values indicating better clustering agreement.

Overcoming challenges

Clustering algorithms may encounter challenges, especially when dealing with high-dimensional data and selecting appropriate distance metrics. Here are some tips to address these challenges:

1. Dimensionality reduction. As mentioned earlier, reduce the dimensionality of the data using techniques like PCA to overcome the curse of dimensionality and improve clustering performance.
2. Feature engineering. Engineer informative features that capture the underlying patterns in the data, helping the clustering algorithm to better differentiate between clusters.
3. Distance metric selection. Choose an appropriate distance metric based on the nature of the data and the characteristics of the problem. Common distance metrics include Euclidean distance, Manhattan distance, and cosine similarity.
4. Experimentation and iteration. Experiment with different clustering algorithms, parameters, and preprocessing techniques to find the optimal solution. Iteratively refine the approach based on evaluation results and domain knowledge.

Clustering algorithms are an effective tool that drives innovation across many domains: from marketing to self-piloting vehicles. If you want to continue learning about ML, keep reading our blog.

Read more:

• Reinforcement Learning: How It Works
• Feature Engineering for Machine Learning
• Dimensions and Haskell: Introduction

Originally published at https://serokell.io.

The market for AI in healthcare is expanding and is projected to grow in the coming years. This technology has already demonstrated significant capabilities in diagnostics and biomedical research. AI’s application in healthcare spans various areas, including early disease detection, personalized medicine, robotic surgeries, and patient management systems. Understanding its current state and potential future developments is crucial for healthcare professionals and policymakers.

In this blog post, we provide an overview of AI applications in healthcare, based on a webinar organized by the Instituto Universitario Piaget (Viseu) and presented by Serokell’s AI Team Lead, Ivan Smetannikov.

What are the general applications of AI in medicine?

AI finds applications across all aspects of healthcare, from diagnostics and online consulting to treatment and healthcare management. By efficiently predicting patient outcomes and customizing treatments to individual needs, it holds promise in combating diseases such as cancer, Alzheimer’s, heart disease, and diabetes at early stages, as well as discovering cures for previously untreatable diseases. In other words, AI can potentially contribute to saving thousands or even millions of lives and enhancing the health of patients with chronic conditions.

Below are several examples of AI implementation in medtech:

Diagnostics

AI models trained on visual data from diseases at different stages can recognize the signals of such illnesses early.

Personalized medicine

AI analyzes patients’ genetic information and medical history to assist doctors in creating customized medications based on their unique genetic makeup, maximizing treatment effectiveness for individuals.

Drug discovery

AI algorithms calculate the probabilities of drug effectiveness, allowing the weakest methods to be dismissed from further investigation, thus accelerating the drug discovery process.

Wearable devices

AI-equipped devices continuously monitor vital signs, predicting potential health issues before they become serious. This is particularly important for managing conditions like diabetes and heart disease.

Healthcare management

Artificial intelligence automates routine tasks such as medical reporting and administrative duties, reducing bureaucratic workloads and streamlining healthcare management.

What ML methods are used in medicine?

Although many advanced AI/ML models are used in other industries, the list is limited for medtech and healthcare. In this field, we only use methods that can guarantee the highest level of validity (probability) and transparent results interpretation.

To perform analytical and predictive tasks, three main types of machine learning analysis are used:

• Classification: For example, it can be used to sort screening data into homogeneous categories-with or without anomalies anomalies-for diagnosing diseases.
• Regression: Predicts values, such as forecasting available hospital beds based on the average treatment lengths of patients and their treatment prescriptions.
• Clustering: Groups similar data points. This is helpful in patient segmentation and the development of personalized treatment plans.

Those three types of analysis are applied to the following tasks:

• Anomaly detection: Identifies unusual data points, such as atypical patient vital signs or a complex set of symptoms.
• Recommendation systems: Suggest relevant items to users. They recommend treatment options, lifestyle changes, or preventive measures based on a patient’s health history and current condition.
• Active learning: Enhances diagnostic models by selecting the most informative samples for human experts to label, improving model accuracy.
• Natural Language Processing (NLP): Deals with interactions between humans and natural language algorithms. It is used in healthcare for extracting information from clinical notes and patient records, and facilitating communication through chatbots and virtual assistants.
• Time series analysis: Predicts trends based on monitoring patients’ conditions. This is essential for forecasting various health-related problems.
• Reinforcement learning: Involves AI agents making decisions to maximize rewards. Optimizes treatment protocols personalized for patient care based on feedback and behavior.
• Dimensionality reduction: Simplifies the analysis of large datasets by retaining only essential information. It Improves interpretability of results and aids in the search for new compounds.

Generative AI

All the tasks described above are performed by the most common form of artificial intelligence today, generative AI.

Generative AI (GenAI) refers to artificial intelligence systems that can create medical reports, recognize diagnostic images, or recommend treatment plans based on learning patterns from existing healthcare data. These systems use advanced models like neural networks to generate outputs.

To enhance the efficiency of generative AI, traditional algorithms can be incorporated into these systems to solve specific sub-problems or optimize certain components in healthcare applications.

• Hidden Markov Models (HMMs): Used for sequence prediction and generation, particularly in speech and language modeling.
• Gaussian Mixture Models (GMMs): Employed for clustering, density estimation, and as components in more complex generative models like variational autoencoders.
• Bayesian Networks: Provide a framework for reasoning under uncertainty, useful in generative AI for creating models that capture dependencies between variables. Read more about Bayesian networks in our blog post.
• Autoregressive models: Utilized to model and generate time series data.
• Markov Chain Monte Carlo (MCMC) methods: Applied for sampling from complex, high-dimensional distributions, which is essential in training and inference of probabilistic models.
• Latent Dirichlet Allocation (LDA): Used for topic modeling, which can help in generating contextually relevant text and understanding document structures.
• Genetic Algorithms and Evolutionary Computation optimization methods: Employedfor optimizing neural network architectures and hyperparameters, as well as evolving creative solutions and designs.

Limitations of GenAI

When we speak of generative AI in healthcare, we most often refer to Large Language Models (LLMs), the most well-known example of which is ChatGPT.

Large Language Models (LLMs) operate by predicting the next word in a sequence, using the context of the preceding dialogue. This has several limitations.

1. The fundamental limitation of LLMs lies in their nature as** probabilistic models**. They generate responses by selecting the most statistically likely words based on their training data, rather than through genuine comprehension of the question or subject matter.
2. LLMs struggle to retain information beyond their context window. The transformer architecture, which underlies most LLMs, inherently loses context over time. This is particularly problematic with large datasets, such as comprehensive medical records. Consequences include:

• Degraded prediction quality for information outside the context window
• Potential for “hallucinations” or fabricated information when the model lacks necessary context.

1. Another limitation to be aware of is the “world model” of LLMs: their accumulated knowledge is reconstructed from texts, not from real-world experiences, leading to potential errors.
2. In the healthcare sector, LLMs can be provided as** external APIs** (e.g., OpenAI), raising concerns about data privacy and security, or run locally within an organization to maintain control over sensitive data.

To address all these concerns and overcome the limitations, more AI research is needed. That’s why investments in this project are vital.

AI in healthcare market overview

According to Statista, the global AI in healthcare market was worth around 11 billion U.S. dollars in 2021. It is forecasted to reach almost $188 billion by 2030, with a compound annual growth rate of 37 percent from 2022 to 2030.

Fortune Business Insights expects the market to grow from $27.69 billion in 2024 to $490.96 billion by 2032.

Binariks provides similar estimates, reporting the market size at $16.3 billion in 2022 and projecting it to reach $173.55 billion by 2029.

How is AI used for drug discovery?

Now that we have covered the working principles of AI, let’s explore how they are applied in one specific healthcare field-drug discovery.

The traditional process of bringing a new drug to market is highly time-consuming and expensive, often taking 10 to 15 years and costing up to $1 billion, with high failure rates, especially during clinical trials. AI significantly increases efficiency, reduces costs, and decreases failure rates at various stages. Below, we explain with a real example how artificial intelligence aids in each stage of the drug discovery process.

Target identification and validation

The first stage is target identification and validation, which involves identifying a biological target, usually a protein, implicated in a disease, and validating its role.

AI helps in druggability assessment by exploring the properties of proteins and assessing their stability for future compound screens. It analyzes vast amounts of biological and genetic data to identify potential disease mechanisms and drug targets, significantly reducing the search time and improving the overall efficiency.

Hit discovery

The next stage is hit discovery, a broad search for potential compounds with the desired biological activity, using high-throughput screening and large compound libraries. The goal is to generate a vast number of compounds, though only a small fraction will show activity. AI streamlines the hit discovery phase by predicting the activity of chemical compounds against specific biological targets.

Lead optimization

In the lead optimization phase, potential drug candidates are determined, and their properties are improved. AI is used to predict ADME profiles (absorption, distribution, metabolism, excretion, and toxicity), forecast the effects of molecular modifications, and model compound properties, reducing the need for extensive physical synthesis and testing.

Moreover, AI can analyze complex structure-activity relationship data, suggesting modifications to enhance compound efficacy and reduce toxicity. It also helps predict and mitigate unforeseen safety issues and identify biomarkers for drug response.

Preclinical testing

The next stage is preclinical testing, which consists in evaluating selected and optimized drug candidates in vitro (test tubes) and in vivo (on animals) to validate their safety and efficacy. AI models predict how new drugs will behave in humans.

Clinical trials

AI helps design clinical trials in the optimal way by predicting various side effects, dosing, and efficacy for different groups. This reduces the size of test groups and helps in selecting underrepresented patients for trials.

Regulatory approval

The regulatory approval stage, handled by the FDA in the US, requires proof that the medication can be marketed. AI can analyze large volumes of data to quantify outcomes, ensure quality control, and detect outliers, inconsistencies, and missing values. It also automates the generation of submission documents, speeding up the approval process.

Post-market surveillance

Post-market surveillance involves observing medical products on the market to understand their performance in a broader population. AI plays a crucial role in this process by monitoring real-time data from wearable devices and patient registries to detect adverse events and analyze patient feedback. It can also pinpoint higher-risk patients, enabling targeted monitoring. Additionally, automated reporting can be used for compliance checks to ensure safety protocols are updated promptly and stakeholders are informed of regulatory changes.

AI-powered drug discovery use cases

Now, let’s examine some of the most interesting and promising use cases of AI in drug discovery.

Prediction of binding affinity: AtomNet

AtomNet is a deep learning application that uses 3D convolutional neural networks to predict the binding affinity of small molecules to proteins. It captures detailed information about binding pockets and interactions and identifies high-affinity molecules from virtual screening libraries. It can also assist in the lead optimization step by predicting how specific chemical modifications might affect binding affinity, as well as in drug repurposing.

Drug candidates identification: Insilico Medicine

Insilico Medicine is an AI-driven platform that generates new molecular structures and evaluates their potential as drug candidates. It employs generative adversarial networks and reinforcement learning to identify promising drug candidates much faster than traditional methods. Reports indicate it can go from initial stages to testing in less than 46 days, whereas the traditional process may take years.

Compounds testing: Recursion

Recursion combines AI with a high-throughput screening system to map cellular biology and identify potential treatments. Their platform can test thousands of compounds across multiple biological contexts simultaneously, using automated microscope image analysis and screening. Computer vision algorithms detect changes in cellular phenotypes, identify patterns, and predict potential treatments.

Protein structure prediction: Alpha Fold

AlphaFold is a protein structure database developed by Google DeepMind and EMBL-EBI (European Bioinformatics Institute). It allows researchers to predict the 3D structures of proteins with high accuracy using a transformer model.

The machine learning model has two main components: one predicts the distance and angles between pairs of amino acids using an attention-based network, and the other focuses on relevant parts of amino acid sequences to predict the structured cell. Its predictions have been verified through methods like X-ray crystallography and have been significant in the field, especially for generating new proteins and drugs.

Halicin repurposing: MIT

Researchers at MIT used graph neural networks to analyze hundreds of millions of chemical compounds in just three days, discovering that halicin, initially used for diabetes treatment, is effective against antibiotic-resistant strains.

Serokell’s work in AI research in medicine

Serokell has extensive experience in the healthcare sector, particularly in virology. We collaborated with a major pharmaceutical company to develop models for automated feature selection, specifically addressing the problem of dementia risk reduction for cancer drug synthesis. We created an algorithm that selects the most relevant genes from tens of thousands. Our solution significantly improved efficiency by reducing the time required for model building and result generation from several weeks to just a few days. It also eliminated the need for expensive GPU servers, making the process more cost-effective by utilizing CPUs instead.

Additionally, our ML team worked on drug repurposing projects with Elsevier, employing graph neural networks to predict interactions between small molecules and diseases. Elsevier provided a dataset of thousands of research papers condensed into a graph format. Our application, powered by graph neural networks, analyzed relationships within the dataset, matching papers related to diseases, proteins, and small molecules, and identifying missing edges-a common problem in graph analysis.

AI safety in healthcare

The major safety concerns for AI use in healthcare include transparency, bias, clinical validation, regulatory compliance, and data privacy.

Transparency

The “black box” nature of deep learning models necessitates additional measures for their use in healthcare. Two main approaches are possible here:

• Developing AI systems that explain their decisions in understandable terms,
• Creating low-accuracy, high-precision systems that can be validated by humans.

Bias

Ensuring that datasets are diverse and representative is crucial, as AI performs better with well-represented data, reducing the risk of disparities in treatment and diagnosis across different patient populations.

Clinical validation

Clinical validation is also necessary before deploying AI tools in real-world scenarios. This involves conducting rigorous trials and studies to assess their performance, accuracy, and reliability in clinical settings.

Regulatory compliance and data privacy

Given the sensitivity of patient data, robust systems are needed to prevent hacking and data leakage. Compliance with regulations such as HIPAA in the United States and GDPR in Europe is essential to ensure that patient data is handled securely and ethically. This involves implementing strong encryption methods, secure data storage solutions, and regular audits to identify and mitigate potential vulnerabilities. Additionally, ensuring transparency in data usage and obtaining proper patient consent are critical components in maintaining trust and compliance with legal standards.

Conclusion

The integration of AI in healthcare offers a transformative opportunity to enhance patient care and streamline medical and drug discovery processes. However, the importance of human-AI collaboration cannot be overstated. That is why AI should serve as an augmentative tool rather than a replacement for medical experts.

As AI continues to evolve, the education and training of medical professionals in these technologies is becoming increasingly important.

Originally published at https://serokell.io.

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