Representatives from Adobe, Cohere, IBM, Nvidia, Palantir, Salesforce, Scale AI, and Stability attended the White House for the announcement. These eight companies have pledged to play a pivotal role in promoting the development of safe, secure, and trustworthy AI.

The Biden-Harris Administration is actively working on an Executive Order and pursuing bipartisan legislation to ensure the US leads the way in responsible AI development that unlocks its potential while managing its risks.

The commitments made by these companies revolve around three fundamental principles: safety, security, and trust. They have committed to:

1. Ensure products are safe before introduction:

The companies commit to rigorous internal and external security testing of their AI systems before releasing them to the public. This includes assessments by independent experts, helping guard against significant AI risks such as biosecurity, cybersecurity, and broader societal effects.

They will also actively share information on AI risk management with governments, civil society, academia, and across the industry. This collaborative approach will include sharing best practices for safety, information on attempts to circumvent safeguards, and technical cooperation.

2. Build systems with security as a top priority:

The companies have pledged to invest in cybersecurity and insider threat safeguards to protect proprietary and unreleased model weights. Recognising the critical importance of these model weights in AI systems, they commit to releasing them only when intended and when security risks are adequately addressed.

Additionally, the companies will facilitate third-party discovery and reporting of vulnerabilities in their AI systems. This proactive approach ensures that issues can be identified and resolved promptly even after an AI system is deployed.

3. Earn the public’s trust:

To enhance transparency and accountability, the companies will develop robust technical mechanisms – such as watermarking systems – to indicate when content is AI-generated. This step aims to foster creativity and productivity while reducing the risks of fraud and deception.

They will also publicly report on their AI systems’ capabilities, limitations, and areas of appropriate and inappropriate use, covering both security and societal risks, including fairness and bias. Furthermore, these companies are committed to prioritising research on the societal risks posed by AI systems, including addressing harmful bias and discrimination.

These leading AI companies will also develop and deploy advanced AI systems to address significant societal challenges, from cancer prevention to climate change mitigation, contributing to the prosperity, equality, and security of all.

The Biden-Harris Administration’s engagement with these commitments extends beyond the US, with consultations involving numerous international partners and allies. These commitments complement global initiatives, including the UK’s Summit on AI Safety, Japan’s leadership of the G-7 Hiroshima Process, and India’s leadership as Chair of the Global Partnership on AI.

The announcement marks a significant milestone in the journey towards responsible AI development, with industry leaders and the government coming together to ensure that AI technology benefits society while mitigating its inherent risks.

(Photo by Tabrez Syed on Unsplash)

See also: UK’s AI ecosystem to hit £2.4T by 2027, third in global race

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Digital Transformation Week.

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The v3.1 iteration of the benchmark suite has seen record participation, boasting over 13,500 performance results and delivering up to a 40 percent improvement in performance. 

What sets this achievement apart is the diverse pool of 26 different submitters and over 2,000 power results, demonstrating the broad spectrum of industry players investing in AI innovation.

Among the list of submitters are tech giants like Google, Intel, and NVIDIA, as well as newcomers Connect Tech, Nutanix, Oracle, and TTA, who are participating in the MLPerf Inference benchmark for the first time.

David Kanter, Executive Director of MLCommons, highlighted the significance of this achievement:

“Submitting to MLPerf is not trivial. It’s a significant accomplishment, as this is not a simple point-and-click benchmark. It requires real engineering work and is a testament to our submitters’ commitment to AI, to their customers, and to ML.”

MLPerf Inference is a critical benchmark suite that measures the speed at which AI systems can execute models in various deployment scenarios. These scenarios span from the latest generative AI chatbots to the safety-enhancing features in vehicles, such as automatic lane-keeping and speech-to-text interfaces.

The spotlight of MLPerf Inference v3.1 shines on the introduction of two new benchmarks:

• An LLM utilising the GPT-J reference model to summarise CNN news articles garnered submissions from 15 different participants, showcasing the rapid adoption of generative AI.

• An updated recommender benchmark – refined to align more closely with industry practices – employs the DLRM-DCNv2 reference model and larger datasets, attracting nine submissions. These new benchmarks are designed to push the boundaries of AI and ensure that industry-standard benchmarks remain aligned with the latest trends in AI adoption, serving as a valuable guide for customers, vendors, and researchers alike.

Mitchelle Rasquinha, co-chair of the MLPerf Inference Working Group, commented: “The submissions for MLPerf Inference v3.1 are indicative of a wide range of accelerators being developed to serve ML workloads.

“The current benchmark suite has broad coverage among ML domains, and the most recent addition of GPT-J is a welcome contribution to the generative AI space. The results should be very helpful to users when selecting the best accelerators for their respective domains.”

MLPerf Inference benchmarks primarily focus on datacenter and edge systems. The v3.1 submissions showcase various processors and accelerators across use cases in computer vision, recommender systems, and language processing.

The benchmark suite encompasses both open and closed submissions in the performance, power, and networking categories. Closed submissions employ the same reference model to ensure a level playing field across systems, while participants in the open division are permitted to submit a variety of models.

As AI continues to permeate various aspects of our lives, MLPerf’s benchmarks serve as vital tools for evaluating and shaping the future of AI technology.

Find the detailed results of MLPerf Inference v3.1 here.

(Photo by Mauro Sbicego on Unsplash)

See also: GitLab: Developers view AI as ‘essential’ despite concerns

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Digital Transformation Week.

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The Global AI Ecosystem platform is developed with support from AI Industry Analytics (AiiA) and Deep Knowledge Group. Designed as a universally accessible space for community interaction, collaboration, content sharing, and knowledge exchange, it has become a vital hub for AI enthusiasts and professionals.

AiiA, in its Global AI Economy Size Assessment report, conducted groundbreaking research showcasing the rapid expansion of the UK’s AI industry.

With over 8,900 companies operating in the sector, the UK AI economy’s valuation of £1.36 trillion underscores its substantial contribution to the national GDP. Approximately 4,100 investment funds are dedicated to AI, with 600 of them based in the UK.

A robust workforce of 500,000 UK-based AI specialists is driving innovation, solidifying the nation’s position in the global AI landscape. This skilled workforce not only bolsters GDP growth but also acts as a safety net against unemployment.

The UK government’s active prioritisation of its national AI agenda is a significant factor in this remarkable growth. Last month, UK Deputy PM Oliver Dowden called AI the most ‘extensive’ industrial revolution yet.

With 280 ongoing projects harnessing AI technology, the UK’s commitment to AI is clear. AI is a major pillar of the country’s national industrial strategy, making the UK one of the most proactive nations in shaping its AI future.

Dmitry Kaminskiy, Founder of AI Industry Analytics (AiiA) and General Partner of Deep Knowledge Group, said:

“Despite an economic downturn and other challenges, the UK stands as an undoubtable, dynamic, and proactive leader in the global AI arena, having surpassed £1.3 trillion in 2023 and projected to reach £2.4 trillion by 2027.

There is no question that AI is poised to be the major driver for economic growth, fuelling the further development of the entire UK DeepTech industry, and creating a cumulative, systemic, positive impact on the full scope of the nation’s integral infrastructure.”

Key cities like London, Cambridge, Manchester, and Edinburgh have emerged as leading AI hubs, fostering collaboration and providing access to essential resources. With nearly 5,000 AI companies in London alone, it competes with entire countries on the global AI stage and solidifies its European leadership status.

AiiA’s estimation of the UK AI economy size used AI algorithms to map the global AI industry, profiling 50,000 companies, 20,000 investors, 2,000 AI leaders, and 2,500 R&D hubs. Building upon previous reports, it conducted the most comprehensive assessment of the Global AI Economy to date, projecting a global AI economy exceeding £27.2 trillion ($34 trillion) by 2027.

The UK’s position as a hub for science, R&D, DeepTech, and AI governance places it in good stead for leveraging AI as a core engine of technological progress and driving economic growth.

(Image Credit: Global AI Ecosystem)

See also: UK government outlines AI Safety Summit plans

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Digital Transformation Week.

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Dowden highlighted AI’s dual role, emphasising its capacity to augment productivity and streamline mundane tasks. However, he also put the spotlight on the looming threats it poses to democracies worldwide.

in an interview with The Times, Mr Dowden said: “This is a total revolution that is coming. It’s going to totally transform almost all elements of life over the coming years, and indeed, even months, in some cases.

“It is much faster than other revolutions that we’ve seen and much more extensive, whether that’s the invention of the internal combustion engine or the industrial revolution.”

Already making inroads into governmental processes, AI has been adopted for processing asylum claim applications within the UK’s Home Office. The potential for AI-driven automation also extends to reducing paperwork burdens in ministerial decision-making, ultimately enabling swifter and more efficient governance.

Sridhar Iyengar, Managing Director for Zoho Europe, commented:

“As AI continues to develop at a rapid pace, collaboration between government, business, and industry experts is needed to increase education and introduce regulations or guidelines which can guide its ethical use.

Only then can businesses confidently use AI in the right way and understand how to avoid any negative impact.”

While AI can expedite information analysis and facilitate decision-making, Dowden emphasised that the crucial task of making policy choices remains squarely within the human domain. He stressed that the objective is to utilise AI for tasks that it excels at – such as data collation – to facilitate informed decision-making by human leaders.

Discussing the broader economic implications of the AI revolution, Dowden likened the impending shift to the advent of the automobile. He recognised the potential for significant workforce upheaval and asserted that the government’s responsibility lies in aiding citizens’ transition as AI reshapes industries.

Sheila Flavell CBE, COO of FDM Group, explained:

“In order to truly maximise the potential of AI, the UK must prioritise a workforce of technically skilled staff capable of leading the development and deployment of AI to work alongside staff and make their day-to-day roles easier.

People such as graduates, ex-forces and returners are well-placed to play a central role in this workforce through education courses and training in AI, supporting businesses with this rapidly-evolving technology.”

Dowden acknowledged the inherent risks posed by AI’s exponential growth. He warned of the potential for AI to be exploited by malicious actors—ranging from terrorists using it to gain knowledge of dangerous materials, to conducting large-scale hacking operations. 

Referring to a recent breach that exposed the personal details of thousands of officers and staff from the Police Service of Northern Ireland, Dowden said the incident was an “industrial scale breach of data” that was made possible by AI.

Andy Ward, VP of International for Absolute Software, said:

“We are in the midst of an AI revolution and for all the business benefits that AI brings, however, we must also be wary of the potential cybersecurity concerns that come with any new technology.

AI can be used to positive effect when bolstering cyber defences, playing a role in threat detection through data and pattern analysis to identify certain attacks, but we have to acknowledge that malicious actors also have access to AI to increase the sophistication of their threats.“

While urging a measured response to potential AI-driven threats, Dowden emphasised the importance of addressing risks and vulnerabilities proactively. He stressed the need to strike a balance between harnessing AI’s immense potential for societal progress and ensuring that safeguards are in place to counter its misuse.

Earlier this year, the UK announced that it will host a global summit to address AI risks.

(Image Credit: UK Government under CC BY 2.0 license)

See also: Google report highlights AI’s impact on the UK economy

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Cyber Security & Cloud Expo and Digital Transformation Week.

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The announcement, made by Technology Secretary Michelle Donelan, marks a major step forward in harnessing the potential of AI in revolutionising healthcare. The investment will empower 22 winning projects across universities and NHS trusts, from Edinburgh to Surrey, to drive innovation and transform patient care.

Dr Antonio Espingardeiro, IEEE member and software and robotics expert, comments:

“As it becomes more sophisticated, AI can efficiently conduct tasks traditionally undertaken by humans. The potential for the technology within the medical field is huge—it can analyse vast quantities of information and, when coupled with machine learning, search through records and infer patterns or anomalies in data, that would otherwise take decades for humans to analyse.

We are just starting to see the beginning of a new era where machine learning could bring substantial value and transform the traditional role of the doctor. The true capabilities of this technology as an aide to the healthcare sector are yet to be fully realised. In the future, we may even be able to solve of some of the biggest challenges and issues of our time.

One of the standout projects receiving funding is the University College London’s Centre for Interventional and Surgical Sciences. With a grant exceeding £500,000, researchers aim to develop a semi-autonomous surgical robotics platform designed to enhance the removal of brain tumours. This pioneering technology promises to elevate surgical outcomes, minimise complications, and expedite patient recovery times.

“With the increased adoption of AI and robotics, we will soon be able to deliver the scalability that the healthcare sector needs and establish more proactive care delivery,” added Espingardeiro.

University of Sheffield’s project, backed by £463,000, is focused on a crucial aspect of healthcare – chronic nerve pain. Their innovative approach aims to widen and improve treatments for this condition, which affects one in ten adults over 30.

The University of Oxford’s project, bolstered by £640,000, seeks to expedite research into a foundational AI model for clinical risk prediction. By analysing an individual’s existing health conditions, this AI model could accurately forecast the likelihood of future health problems and revolutionise early intervention strategies.

Meanwhile, Heriot-Watt University in Edinburgh has secured £644,000 to develop a groundbreaking system that offers real-time feedback to trainee surgeons practising laparoscopy procedures, also known as keyhole surgeries. This technology promises to enhance the proficiency of aspiring surgeons and elevate the overall quality of healthcare.

Finally, the University of Surrey’s project – backed by £456,000 – will collaborate closely with radiologists to develop AI capable of enhancing mammogram analysis. By streamlining and improving this critical diagnostic process, AI could contribute to earlier cancer detection.

Ayesha Iqbal, IEEE senior member and engineering trainer at the Advanced Manufacturing Training Centre, said:

“The emergence of AI in healthcare has completely reshaped the way we diagnose, treat, and monitor patients.

Applications of AI in healthcare include finding new links between genetic codes, performing robot-assisted surgeries, improving medical imaging methods, automating administrative tasks, personalising treatment options, producing more accurate diagnoses and treatment plans, enhancing preventive care and quality of life, predicting and tracking the spread of infectious diseases, and helping combat epidemics and pandemics.”

With the UK healthcare sector already witnessing AI applications in improving stroke diagnosis, heart attack risk assessment, and more, the £13 million investment is poised to further accelerate transformative healthcare breakthroughs.

Health and Social Care Secretary Steve Barclay commented:

“AI can help the NHS improve outcomes for patients, with breakthroughs leading to earlier diagnosis, more effective treatments, and faster recovery. It’s already being used in the NHS in a number of areas, from improving diagnosis and treatment for stroke patients to identifying those most at risk of a heart attack.

This funding is yet another boost to help the UK lead the way in healthcare research. It comes on top of the £21 million we recently announced for trusts to roll out the latest AI diagnostic tools and £123 million invested in 86 promising tech through our AI in Health and Care Awards.”

However, the announcement was made the same week as NHS waiting lists hit a record high. Prime Minister Rishi Sunak made reducing waiting lists one of his five key priorities for 2023 on which to hold him “to account directly for whether it is delivered.” Hope is being pinned on technologies like AI to help tackle waiting lists.

This pivotal move is accompanied by the nation’s preparations to host the world’s first major international summit on AI safety, underscoring its commitment to responsible AI development.

Scheduled for later this year, the AI safety summit will provide a platform for international stakeholders to collaboratively address AI’s risks and opportunities.

As Europe’s AI leader, and the third-ranking globally behind the USA and China, the UK is well-positioned to lead these discussions and champion the responsible advancement of AI technology.

(Photo by National Cancer Institute on Unsplash)

See also: BSI publishes guidance to boost trust in AI for healthcare

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The event is co-located with Digital Transformation Week.

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The guidance, titled ’Validation framework for the use of AI within healthcare – Specification (BS 30440),’ aims to bolster confidence among clinicians, healthcare professionals, and providers regarding the safe, effective, and ethical development of AI tools.

As the global debate on the appropriate use of AI continues, this auditable standard targets products primarily designed for healthcare interventions, diagnoses, and health condition management.

Jeanne Greathouse, Global Healthcare Director at BSI, said:

“This standard is highly relevant to organisations in the healthcare sector and those interacting with it. As AI becomes the norm, it has the potential to be transformative for healthcare.

With the onset of more innovative AI tools, and AI algorithms’ ability to digest and accurately analyse copious amounts of data, clinicians and health providers can efficiently make informed diagnostic decisions to intervene, prevent, and treat diseases, ultimately improving patients’ quality of life.”

According to forecasts, the global healthcare AI market is expected to surpass $187.95 billion by 2030. However, healthcare providers and clinicians may face challenges in assessing AI products due to time and budget constraints or a lack of in-house capabilities. 

The BS 30440 specification seeks to aid decision-making processes by providing criteria for evaluating healthcare AI products, including clinical benefit, performance standards, safe integration into clinical environments, ethical considerations, and equitable social outcomes.

The standard covers a wide range of healthcare AI products, including regulated medical devices like software used for medical purposes, imaging software, patient-facing products like AI-powered smartphone chatbots, and home monitoring devices. It applies to products and technologies utilising AI elements – including machine learning – and is relevant to both AI system suppliers and product auditors.

The development of this specification involved collaboration among a panel of experts, including clinicians, software engineers, AI specialists, ethicists, and healthcare leaders. The guidance draws from existing literature and best practices, translating complex functionality assessments into an auditable framework for AI system conformity.

Healthcare organisations will be able to mandate BS 30440 certification in their procurement processes to ensure adherence to these recognized standards.

Scott Steedman, Director General for Standards at BSI, commented:

“The new guidance can help build digital trust in cutting-edge tools that represent enormous potential benefit to patients, and the professionals diagnosing and treating them.

AI has the potential to shape our future in a positive way and we all need confidence in the tools being developed, especially in healthcare.

This specification, which is auditable, can help guide everyone from doctors to healthcare leaders and patients to choose AI products that are safe, effective, and ethically produced.”

The specification addresses the need for an agreed validation framework for AI development and clinical evaluation in healthcare. It builds on a framework initially piloted at Guy’s and St. Thomas Cancer Centre and later revised through discussions with stakeholders involved in AI and machine learning.

With the publication of this guidance, BSI seeks to instil confidence in AI products used in healthcare and empower doctors, healthcare leaders, and patients to make informed and ethical choices for improved patient care and overall societal benefit.

As AI continues to shape the future of healthcare, adherence to recognised standards will play a vital role in ensuring the safe and effective integration of AI technologies in medical practice.

(Photo by Owen Beard on Unsplash)

See also: AI regulation: A pro-innovation approach – EU vs UK

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According to a recent report by Harnham, a leading data and analytics recruitment agency in the UK, the demand for ML engineering roles has been steadily rising over the past few years.

Recently, there’s been a shift towards MLOps professionals who possess the skills to bridge the gap between data scientists and data engineers, thereby optimising the deployment of ML models.

Harnham’s report provides comprehensive insights into the salaries and day rates of various data science roles across the UK.

Technical leads/managers in computer vision, data science, deep learning & AI, ML engineering, MLOps, and natural language processing are earning annual base salaries ranging from £44,000 to £120,000, depending on experience and location.

In addition to competitive compensation, data science professionals are seeking specific benefits to enhance their job satisfaction.

The top five desirable benefits include remote working options, bonuses, health insurance, flexible working hours, and shares. These perks play a crucial role in attracting and retaining top talent in the data science sector.

The report also sheds light on some critical trends and statistics in the industry.

25 percent of professionals cited a non-competitive salary/rate as the top reason for leaving a role, followed closely by a lack of career progression (24%) and a “better opportunity” coming along (22%).

The number of female professionals in the field has increased from 22 percent last year, indicating a positive shift towards greater gender diversity in data science.

While the field of data science continues to evolve rapidly, professionals are keen to explore new opportunities.

One finding from the report reveals that data science professionals are the most likely to leave their current roles if the right opportunity arises. The ongoing talent shortage means that relevant expertise is in high demand and many opportunities are available.

Advancements in AI and ML are transforming the landscape and creating exciting new job opportunities. As the demand for data professionals continues to surge, companies must adapt to remain competitive in attracting and retaining top talent in this thriving field.

For more information and in-depth data on data science salaries and trends in the UK, refer to the Harnham Data & AI Salary Guide for 2023.

(Photo by Ben Rosett on Unsplash)

See also: Universities want to ensure staff and students are ‘AI-literate’

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The event is co-located with Digital Transformation Week.

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This strategic partnership aims to empower AI teams with powerful computing resources on-demand, enhancing their capabilities and streamlining their operations.

The collaboration combines the strengths of three industry leaders: Graphcore, renowned for its Intelligence Processing Units (IPUs) hardware; UbiOps, a powerful machine learning operations (MLOps) platform; and Gcore Cloud, known for its robust cloud infrastructure.

By leveraging these cutting-edge technologies, Gcore Cloud presents AI teams with a seamless experience, making it effortless to utilise IPUs for specific AI tasks while benefiting from UbiOps’ MLOps features such as model versioning, governance, and monitoring.

Andre Reitenbach, CEO at Gcore, commented:

“The collaboration between Gcore, Graphcore, and UbiOps brings a seamless experience for AI teams. This enables effortless utilisation of Gcore’s cloud infrastructure with Graphcore’s IPUs on the UbiOps platform. This means that users can take advantage of the exceptional computational capabilities of IPUs for their specific AI tasks. Also, users can leverage UbiOps’ out-of-the-box MLOps features such as model versioning, governance, and monitoring.

These features help teams to accelerate time to market with AI solutions, save on computing resource costs, and efficiently use them with on-demand hardware scaling. We’re thrilled about this partnership’s potential to enable AI projects to succeed and reach their goals.”

To showcase the significant advantages of IPUs over other devices, Gcore conducted benchmarking tests on three different compute resources: CPUs, GPUs, and IPUs.

Gcore trained a Convolutional Neural Network (CNN) – a model designed for image analysis – using the CIFAR-10 dataset containing 60,000 labelled images, on these devices.

The results were striking, with IPUs and GPUs significantly outperforming CPUs in training speed. Even with minimal optimisation, IPUs demonstrated a clear advantage over GPUs, enabling even shorter training times:

This collaboration offers AI teams unparalleled access to powerful hardware tailor-made for demanding AI and ML workloads.

By integrating Gcore Cloud, Graphcore’s IPUs, and UbiOps’ MLOps platform, teams can work more efficiently, cost-effectively, and scale their hardware as needed. The combined offering enables AI projects to realise their full potential, driving innovation and progress in the AI industry.

With this strategic alliance, Gcore, Graphcore, and UbiOps are poised to make advanced resources more accessible and empower AI teams worldwide to achieve their goals.

(Photo by Nathan Dumlao on Unsplash)

See also: Damian Bogunowicz, Neural Magic: On revolutionising deep learning with CPUs

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This strategic move aims to make generative AI accessible to organisations of all sizes, allowing them to develop, possess, and safeguard their own generative AI models using their own data. 

The acquisition, valued at ~$1.3 billion – inclusive of retention packages – showcases Databricks’ commitment to democratising AI and reinforcing the company’s Lakehouse platform as a leading environment for building generative AI and LLMs.

Naveen Rao, Co-Founder and CEO at MosaicML, said:

“At MosaicML, we believe in a world where everyone is empowered to build and train their own models, imbued with their own opinions and viewpoints — and joining forces with Databricks will help us make that belief a reality.

We started MosaicML to solve the hard engineering and research problems necessary to make large-scale training more accessible to everyone. With the recent generative AI wave, this mission has taken centre stage.

Together with Databricks, we will tip the scales in the favour of many — and we’ll do it as kindred spirits: researchers turned entrepreneurs sharing a similar mission. We look forward to continuing this journey together with the AI community.”

MosaicML has gained recognition for its cutting-edge MPT large language models, with millions of downloads for MPT-7B and the recent release of MPT-30B.

The platform has demonstrated how organisations can swiftly construct and train their own state-of-the-art models cost-effectively by utilising their own data. Esteemed customers like AI2, Generally Intelligent, Hippocratic AI, Replit, and Scatter Labs have leveraged MosaicML for a diverse range of generative AI applications.

The primary objective of this acquisition is to provide organisations with a simple and rapid method to develop, own, and secure their models. By combining the capabilities of Databricks’ Lakehouse Platform with MosaicML’s technology, customers can maintain control, security, and ownership of their valuable data without incurring exorbitant costs.

MosaicML’s automatic optimisation of model training enables 2x-7x faster training compared to standard approaches, and the near linear scaling of resources allows for the training of multi-billion-parameter models within hours. Consequently, Databricks and MosaicML aim to reduce the cost of training and utilising LLMs from millions to thousands of dollars.

The integration of Databricks’ unified Data and AI platform with MosaicML’s generative AI training capabilities will result in a robust and flexible platform capable of serving the largest organisations and addressing various AI use cases.

Upon the completion of the transaction, the entire MosaicML team – including its renowned research team – is expected to join Databricks.

MosaicML’s machine learning and neural networks experts are at the forefront of AI research, striving to enhance model training efficiency. They have contributed to popular open-source foundational models like MPT-30B, as well as the training algorithms powering MosaicML’s products.

The MosaicML platform will be progressively supported, scaled, and integrated to provide customers with a seamless unified platform where they can build, own, and secure their generative AI models. The partnership between Databricks and MosaicML empowers customers with the freedom to construct their own models, train them using their unique data, and develop differentiating intellectual property for their businesses.

The completion of the proposed acquisition is subject to customary closing conditions, including regulatory clearances.

(Photo by Glen Carrie on Unsplash)

See also: MosaicML’s latest models outperform GPT-3 with just 30B parameters

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The event is co-located with Digital Transformation Week.

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At its core, Predibase is a declarative machine learning platform that aims to streamline the process of developing and deploying machine learning models. The company is on a mission to simplify and democratise machine learning, making it accessible to both expert organisations and developers who are new to the field.

The platform empowers organisations with in-house experts, enabling them to supercharge their capabilities and reduce development times from months to just days. Additionally, it caters to developers who want to integrate machine learning into their products but lack the expertise.

By using Predibase, developers can avoid writing extensive lines of low-level machine learning code and instead work with a simple configuration file – known as a YAML file – which contains just 10 lines specifying the data schema.

Predibase reaches general availability

During the expo, Predibase announced the general availability of its platform.

One of the key features of the platform is its ability to abstract away the complexity of infrastructure provisioning. Users can seamlessly run training, deployment, and inference jobs on a single CPU machine or scale up to 1000 GPU machines with just a few clicks. The platform also facilitates easy integration with various data sources, including data warehouses, databases, and object stores, regardless of the data structure.

“The platform is designed for teams to collaborate on developing models, with each model represented as a configuration that can have multiple versions. You can analyse the differences and performance of the models,” explains Molino.

Once a model meets the required performance criteria, it can be deployed for real-time predictions as a REST endpoint or for batch predictions using SQL-like queries that include prediction capabilities.

Importance of low-code in machine learning

The conversation then shifted to the importance of low-code development in machine learning adoption. Molino emphasised that simplifying the process is essential for wider industry adoption and increased return on investment.

By reducing the development time from months to a matter of days, Predibase lowers the entry barrier for organisations to experiment with new use cases and potentially unlock significant value.

“If every project takes months or even years to develop, organisations won’t be incentivised to explore valuable use cases. Lowering the bar is crucial for experimentation, discovery, and increasing return on investment,” says Molino.

“With a low-code approach, development times are reduced to a couple of days, making it easier to try out different ideas and determine their value.”

Trends in LLMs

The discussion also touched on the rising interest in large language models. Molino acknowledged the tremendous power of these models and their ability to transform the way people think about AI and machine learning.

“These models are powerful and revolutionizing the way people think about AI and machine learning. Previously, collecting and labelling data was necessary before training a machine learning model. But now, with APIs, people can query the model directly and obtain predictions, opening up new possibilities,” explains Molino.

However, Molino highlighted some limitations, such as the cost and scalability of per-query pricing models, the relatively slow inference speeds, and concerns about data privacy when using third-party APIs.

In response to these challenges, Predibase is introducing a mechanism that allows customers to deploy their models in a virtual private cloud, ensuring data privacy and providing greater control over the deployment process.

Common mistakes

As more businesses venture into machine learning for the first time, Molino shared his insights into some of the common mistakes they make. He emphasised the importance of understanding the data, the use case, and the business context before diving headfirst into development. 

“One common mistake is having unrealistic expectations and a mismatch between what they expect and what is actually achievable. Some companies jump into machine learning without fully understanding the data or the use case, both technically and from a business perspective,” says Molino.

Predibase addresses this challenge by offering a platform that facilitates hypothesis testing, integrating data understanding and model training to validate the suitability of models for specific tasks. With guardrails in place, even users with less experience can engage in machine learning with confidence.

The general availability launch of Predibase’s platform marks an important milestone in their mission to democratise machine learning. By simplifying the development process, Predibase aims to unlock the full potential of machine learning for organisations and developers alike.

You can watch our full interview with Molino below:

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The event is co-located with Digital Transformation Week.

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