Imagine chatting with an AI about a health concern, and instead of just processing your words, it could actually look at the photo of that worrying rash or make sense of your ECG printout. That’s what Google is aiming for.

We already knew AMIE showed promise in text-based medical chats, thanks to earlier work published in Nature. But let’s face it, real medicine isn’t just about words.

Doctors rely heavily on what they can see – skin conditions, readings from machines, lab reports. As the Google team rightly points out, even simple instant messaging platforms “allow static multimodal information (e.g., images and documents) to enrich discussions.”

Text-only AI was missing a huge piece of the puzzle. The big question, as the researchers put it, was “Whether LLMs can conduct diagnostic clinical conversations that incorporate this more complex type of information.”

Google teaches AMIE to look and reason

Google’s engineers have beefed up AMIE using their Gemini 2.0 Flash model as the brains of the operation. They’ve combined this with what they call a “state-aware reasoning framework.” In plain English, this means the AI doesn’t just follow a script; it adapts its conversation based on what it’s learned so far and what it still needs to figure out.

It’s close to how a human clinician works: gathering clues, forming ideas about what might be wrong, and then asking for more specific information – including visual evidence – to narrow things down.

“This enables AMIE to request relevant multimodal artifacts when needed, interpret their findings accurately, integrate this information seamlessly into the ongoing dialogue, and use it to refine diagnoses,” Google explains.

Think of the conversation flowing through stages: first gathering the patient’s history, then moving towards diagnosis and management suggestions, and finally follow-up. The AI constantly assesses its own understanding, asking for that skin photo or lab result if it senses a gap in its knowledge.

To get this right without endless trial-and-error on real people, Google built a detailed simulation lab.

Google created lifelike patient cases, pulling realistic medical images and data from sources like the PTB-XL ECG database and the SCIN dermatology image set, adding plausible backstories using Gemini. Then, they let AMIE ‘chat’ with simulated patients within this setup and automatically check how well it performed on things like diagnostic accuracy and avoiding errors (or ‘hallucinations’).

The virtual OSCE: Google puts AMIE through its paces

The real test came in a setup designed to mirror how medical students are assessed: the Objective Structured Clinical Examination (OSCE).

Google ran a remote study involving 105 different medical scenarios. Real actors, trained to portray patients consistently, interacted either with the new multimodal AMIE or with actual human primary care physicians (PCPs). These chats happened through an interface where the ‘patient’ could upload images, just like you might in a modern messaging app.

Afterwards, specialist doctors (in dermatology, cardiology, and internal medicine) and the patient actors themselves reviewed the conversations.

The human doctors scored everything from how well history was taken, the accuracy of the diagnosis, the quality of the suggested management plan, right down to communication skills and empathy—and, of course, how well the AI interpreted the visual information.

Surprising results from the simulated clinic

Here’s where it gets really interesting. In this head-to-head comparison within the controlled study environment, Google found AMIE didn’t just hold its own—it often came out ahead.

The AI was rated as being better than the human PCPs at interpreting the multimodal data shared during the chats. It also scored higher on diagnostic accuracy, producing differential diagnosis lists (the ranked list of possible conditions) that specialists deemed more accurate and complete based on the case details.

Specialist doctors reviewing the transcripts tended to rate AMIE’s performance higher across most areas. They particularly noted “the quality of image interpretation and reasoning,” the thoroughness of its diagnostic workup, the soundness of its management plans, and its ability to flag when a situation needed urgent attention.

Perhaps one of the most surprising findings came from the patient actors: they often found the AI to be more empathetic and trustworthy than the human doctors in these text-based interactions.

And, on a critical safety note, the study found no statistically significant difference between how often AMIE made errors based on the images (hallucinated findings) compared to the human physicians.

Technology never stands still, so Google also ran some early tests swapping out the Gemini 2.0 Flash model for the newer Gemini 2.5 Flash.

Using their simulation framework, the results hinted at further gains, particularly in getting the diagnosis right (Top-3 Accuracy) and suggesting appropriate management plans.

While promising, the team is quick to add a dose of realism: these are just automated results, and “rigorous assessment through expert physician review is essential to confirm these performance benefits.”

Important reality checks

Google is commendably upfront about the limitations here. “This study explores a research-only system in an OSCE-style evaluation using patient actors, which substantially under-represents the complexity… of real-world care,” they state clearly. 

Simulated scenarios, however well-designed, aren’t the same as dealing with the unique complexities of real patients in a busy clinic. They also stress that the chat interface doesn’t capture the richness of a real video or in-person consultation.

So, what’s the next step? Moving carefully towards the real world. Google is already partnering with Beth Israel Deaconess Medical Center for a research study to see how AMIE performs in actual clinical settings with patient consent.

The researchers also acknowledge the need to eventually move beyond text and static images towards handling real-time video and audio—the kind of interaction common in telehealth today.

Giving AI the ability to ‘see’ and interpret the kind of visual evidence doctors use every day offers a glimpse of how AI might one day assist clinicians and patients. However, the path from these promising findings to a safe and reliable tool for everyday healthcare is still a long one that requires careful navigation.

(Photo by Alexander Sinn)

See also: Are AI chatbots really changing the world of work?

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The first model from this latest generation is an experimental version of Gemini 2.5 Pro, which DeepMind says has achieved state-of-the-art results across a wide range of benchmarks.

According to Koray Kavukcuoglu, CTO of Google DeepMind, the Gemini 2.5 models are “thinking models”.  This signifies their capability to reason through their thoughts before generating a response, leading to enhanced performance and improved accuracy.    

The capacity for “reasoning” extends beyond mere classification and prediction, Kavukcuoglu explains. It encompasses the system’s ability to analyse information, deduce logical conclusions, incorporate context and nuance, and ultimately, make informed decisions.

DeepMind has been exploring methods to enhance AI’s intelligence and reasoning capabilities for some time, employing techniques such as reinforcement learning and chain-of-thought prompting. This groundwork led to the recent introduction of their first thinking model, Gemini 2.0 Flash Thinking.    

“Now, with Gemini 2.5,” says Kavukcuoglu, “we’ve achieved a new level of performance by combining a significantly enhanced base model with improved post-training.”

Google plans to integrate these thinking capabilities directly into all of its future models—enabling them to tackle more complex problems and support more capable, context-aware agents.    

Gemini 2.5 Pro secures the LMArena leaderboard top spot

Gemini 2.5 Pro Experimental is positioned as DeepMind’s most advanced model for handling intricate tasks. As of writing, it has secured the top spot on the LMArena leaderboard – a key metric for assessing human preferences – by a significant margin, demonstrating a highly capable model with a high-quality style:

Gemini 2.5 is a ‘pro’ at maths, science, coding, and reasoning

Gemini 2.5 Pro has demonstrated state-of-the-art performance across various benchmarks that demand advanced reasoning.

Notably, it leads in maths and science benchmarks – such as GPQA and AIME 2025 – without relying on test-time techniques that increase costs, like majority voting. It also achieved a state-of-the-art score of 18.8% on Humanity’s Last Exam, a dataset designed by subject matter experts to evaluate the human frontier of knowledge and reasoning.

DeepMind has placed significant emphasis on coding performance, and Gemini 2.5 represents a substantial leap forward compared to its predecessor, 2.0, with further improvements in the pipeline. 2.5 Pro excels in creating visually compelling web applications and agentic code applications, as well as code transformation and editing.

On SWE-Bench Verified, the industry standard for agentic code evaluations, Gemini 2.5 Pro achieved a score of 63.8% using a custom agent setup. The model’s reasoning capabilities also enable it to create a video game by generating executable code from a single-line prompt.

Building on its predecessors’ strengths

Gemini 2.5 builds upon the core strengths of earlier Gemini models, including native multimodality and a long context window. 2.5 Pro launches with a one million token context window, with plans to expand this to two million tokens soon. This enables the model to comprehend vast datasets and handle complex problems from diverse information sources, spanning text, audio, images, video, and even entire code repositories.    

Developers and enterprises can now begin experimenting with Gemini 2.5 Pro in Google AI Studio. Gemini Advanced users can also access it via the model dropdown on desktop and mobile platforms. The model will be rolled out on Vertex AI in the coming weeks.    

Google DeepMind encourages users to provide feedback, which will be used to further enhance Gemini’s capabilities.

(Photo by Anshita Nair)

See also: DeepSeek V3-0324 tops non-reasoning AI models in open-source first

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AlphaProteo can generate new protein binders for diverse target proteins, including VEGF-A, which is associated with cancer and diabetes complications. Notably, this is the first time an AI tool has successfully designed a protein binder for VEGF-A.

The system’s performance is particularly impressive, achieving higher experimental success rates and binding affinities that are up to 300 times better than existing methods across seven target proteins tested:

Trained on vast amounts of protein data from the Protein Data Bank and over 100 million predicted structures from AlphaFold, AlphaProteo has learned the intricacies of molecular binding. Given the structure of a target molecule and preferred binding locations, the system generates a candidate protein designed to bind at those specific sites.

To validate AlphaProteo’s capabilities, the team designed binders for a diverse range of target proteins, including viral proteins involved in infection and proteins associated with cancer, inflammation, and autoimmune diseases. The results were promising, with high binding success rates and best-in-class binding strengths observed across the board.

For instance, when targeting the viral protein BHRF1, 88% of AlphaProteo’s candidate molecules bound successfully in wet lab testing. On average, AlphaProteo binders exhibited 10 times stronger binding than the best existing design methods across the targets tested.

The system’s performance suggests it could significantly reduce the time required for initial experiments involving protein binders across a wide range of applications. However, the team acknowledges that AlphaProteo has limitations, as it was unable to design successful binders against TNFɑ (a protein associated with autoimmune diseases like rheumatoid arthritis.)

To ensure responsible development, Google DeepMind is collaborating with external experts to inform their phased approach to sharing this work and contributing to community efforts in developing best practices—including the NTI’s new AI Bio Forum.

As the technology evolves, the team plans to work with the scientific community to leverage AlphaProteo on impactful biology problems and understand its limitations. They are also exploring drug design applications at Isomorphic Labs.

While AlphaProteo represents a significant step forward in protein design, achieving strong binding is typically just the first step in designing proteins for practical applications. There remain many bioengineering challenges to overcome in the research and development process.

Nevertheless, Google DeepMind’s advancement holds tremendous potential for accelerating progress across a broad spectrum of research, including drug development, cell and tissue imaging, disease understanding and diagnosis, and even crop resistance to pests.

You can find the full AlphaProteo whitepaper here (PDF)

See also: Paige and Microsoft unveil next-gen AI models for cancer diagnosis

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In May, several high-profile employees departed from the company, including Jan Leike, the former head of OpenAI’s “super alignment” efforts to ensure advanced AI systems remain aligned with human values. Leike’s exit came shortly after OpenAI unveiled its new flagship GPT-4o model, which it touted as “magical” at its Spring Update event.

According to reports, Leike’s departure was driven by constant disagreements over security measures, monitoring practices, and the prioritisation of flashy product releases over safety considerations.

Leike’s exit has opened a Pandora’s box for the AI firm. Former OpenAI board members have come forward with allegations of psychological abuse levelled against CEO Sam Altman and the company’s leadership.

The growing internal turmoil at OpenAI coincides with mounting external concerns about the potential risks posed by generative AI technology like the company’s own language models. Critics have warned about the imminent existential threat of advanced AI surpassing human capabilities, as well as more immediate risks like job displacement and the weaponisation of AI for misinformation and manipulation campaigns.

In response, a group of current and former employees from OpenAI, Anthropic, DeepMind, and other leading AI companies have penned an open letter addressing these risks.

“We are current and former employees at frontier AI companies, and we believe in the potential of AI technology to deliver unprecedented benefits to humanity. We also understand the serious risks posed by these technologies,” the letter states.

“These risks range from the further entrenchment of existing inequalities, to manipulation and misinformation, to the loss of control of autonomous AI systems potentially resulting in human extinction. AI companies themselves have acknowledged these risks, as have governments across the world, and other AI experts.”

The letter, which has been signed by 13 employees and endorsed by AI pioneers Yoshua Bengio and Geoffrey Hinton, outlines four core demands aimed at protecting whistleblowers and fostering greater transparency and accountability around AI development:

1. That companies will not enforce non-disparagement clauses or retaliate against employees for raising risk-related concerns.
2. That companies will facilitate a verifiably anonymous process for employees to raise concerns to boards, regulators, and independent experts.
3. That companies will support a culture of open criticism and allow employees to publicly share risk-related concerns, with appropriate protection of trade secrets.
4. That companies will not retaliate against employees who share confidential risk-related information after other processes have failed.

“They and others have bought into the ‘move fast and break things’ approach and that is the opposite of what is needed for technology this powerful and this poorly understood,” said Daniel Kokotajlo, a former OpenAI employee who left due to concerns over the company’s values and lack of responsibility.

The demands come amid reports that OpenAI has forced departing employees to sign non-disclosure agreements preventing them from criticising the company or risk losing their vested equity. OpenAI CEO Sam Altman admitted being “embarrassed” by the situation but claimed the company had never actually clawed back anyone’s vested equity.

As the AI revolution charges forward, the internal strife and whistleblower demands at OpenAI underscore the growing pains and unresolved ethical quandaries surrounding the technology.

See also: OpenAI disrupts five covert influence operations

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The new capability allows Gemini 1.5 to process extremely long text passages – up to one million characters – to understand context and meaning. This dwarfs previous AI systems like Claude 2.1 and GPT-4 Turbo, which max out at 200,000 and 128,000 tokens respectively:

“Gemini 1.5 Pro achieves near-perfect recall on long-context retrieval tasks across modalities, improves the state-of-the-art in long-document QA, long-video QA and long-context ASR, and matches or surpasses Gemini 1.0 Ultra’s state-of-the-art performance across a broad set of benchmarks,” said Google researchers in a technical paper (PDF).

The efficiency of Google’s latest model is attributed to its innovative Mixture-of-Experts (MoE) architecture.

“While a traditional Transformer functions as one large neural network, MoE models are divided into smaller ‘expert’ neural networks,” explained Demis Hassabis, CEO of Google DeepMind.

“Depending on the type of input given, MoE models learn to selectively activate only the most relevant expert pathways in its neural network. This specialisation massively enhances the model’s efficiency.”

To demonstrate the power of the 1M token context window, Google showed how Gemini 1.5 could ingest the entire 326,914-token Apollo 11 flight transcript and then accurately answer specific questions about it. It also summarised key details from a 684,000-token silent film when prompted.

Google is initially providing developers and enterprises free access to a limited Gemini 1.5 preview with a one million token context window. A 128,000 token general release for the public will come later, along with pricing details.

For now, the one million token capability remains experimental. But if it lives up to its early promise, Gemini 1.5 could set a new standard for AI’s ability to understand complex, real-world text.

Developers interested in testing Gemini 1.5 Pro can sign up in AI Studio. Google says that enterprise customers can reach out to their Vertex AI account team.

(Image Credit: Google)

See also: Amazon trains 980M parameter LLM with ’emergent abilities’

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Their new ‘SELF-DISCOVER’ prompting framework – published this week on arXiV and Hugging Face – represents a significant leap beyond existing techniques, potentially revolutionising the performance of leading models such as OpenAI’s GPT-4 and Google’s PaLM 2.

The framework promises substantial enhancements in tackling challenging reasoning tasks. It demonstrates remarkable improvements, boasting up to a 32% performance increase compared to traditional methods like Chain of Thought (CoT). This novel approach revolves around LLMs autonomously uncovering task-intrinsic reasoning structures to navigate complex problems.

At its core, the framework empowers LLMs to self-discover and utilise various atomic reasoning modules – such as critical thinking and step-by-step analysis – to construct explicit reasoning structures.

By mimicking human problem-solving strategies, the framework operates in two stages:

• Stage one involves composing a coherent reasoning structure intrinsic to the task, leveraging a set of atomic reasoning modules and task examples.
• During decoding, LLMs then follow this self-discovered structure to arrive at the final solution.

In extensive testing across various reasoning tasks – including Big-Bench Hard, Thinking for Doing, and Math – the self-discover approach consistently outperformed traditional methods. Notably, it achieved an accuracy of 81%, 85%, and 73% across the three tasks with GPT-4, surpassing chain-of-thought and plan-and-solve techniques.

However, the implications of this research extend far beyond mere performance gains.

By equipping LLMs with enhanced reasoning capabilities, the framework paves the way for tackling more challenging problems and brings AI closer to achieving general intelligence. Transferability studies conducted by the researchers further highlight the universal applicability of the composed reasoning structures, aligning with human reasoning patterns.

As the landscape evolves, breakthroughs like the SELF-DISCOVER prompting framework represent crucial milestones in advancing the capabilities of language models and offering a glimpse into the future of AI.

(Photo by Victor on Unsplash)

See also: The UK is outpacing the US for AI hiring

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In a new paper in Nature, DeepMind revealed that AlphaGeometry was able to solve 25 out of 30 benchmark geometry problems from past International Mathematical Olympiad (IMO) competitions within the standard time limits. This nearly matches the average score of 26 problems solved by human gold medalists on the same tests.

The AI system combines a neural language model with a rule-bound deduction engine, providing a synergy that enables the system to find solutions to complex geometry theorems.

AlphaGeometry took a revolutionary approach to synthetic data generation by creating one billion random diagrams of geometric objects and deriving relationships between points and lines in each diagram. This process – termed “symbolic deduction and traceback” – resulted in a final training dataset of 100 million unique examples, providing a rich source for training the AI system.

According to DeepMind, AlphaGeometry represents a breakthrough in mathematical reasoning for AI, bringing it closer to the level of human mathematicians. Developing these skills is seen as essential for advancing artificial general intelligence.

Evan Chen, a maths coach and former Olympiad gold medalist, evaluated a sample of AlphaGeometry’s solutions. He said its output was not just correct, but also clean, human-readable proofs using standard geometry techniques—unlike the messy numerical solutions often produced when AI systems brute force maths problems.

While AlphaGeometry only handles the geometry portions of Olympiad tests so far, its skills alone would have been enough to earn a bronze medal on some past exams. DeepMind hopes to continue improving its maths reasoning abilities to the point it could pass the entire multi-subject Olympiad.

Advancing AI’s understanding of mathematics and logic is a key goal for DeepMind and Google. The researchers believe mastering Olympiad problems brings them one step closer towards more generalised artificial intelligence that can automatically discover new knowledge.

(Photo by Dustin Humes on Unsplash)

See also: Stability AI releases Stable Code 3B for enhanced coding assistance

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Available for Vertex AI customers on the allowlist, Imagen 2 enables users to craft and deploy photorealistic images using intuitive tooling and fully-managed infrastructure. 

Developed with Google DeepMind technology, Imagen 2 offers improved image quality and a range of functionalities tailored for specific use cases.

Key features of Imagen 2 include:

• Diverse image generation: Imagen 2 excels in creating high-resolution images from natural language prompts that cater to various user requirements.

• Text rendering in multiple languages: Overcoming common challenges, Imagen 2 supports accurate text rendering in multiple languages.

• Logo generation: Businesses can leverage Imagen 2 to create a variety of creative and realistic logos—with the option to overlay them on products, clothing, business cards, and more.

• Captions and question-answering: Imagen 2’s advanced image understanding capabilities facilitate the creation of descriptive captions and provide detailed answers to questions about image elements.

• Multi-language support: Imagen 2 introduces support for six additional languages in preview, with plans for more in early 2024. This includes the ability to translate between prompt and output.

• Safety measures: Imagen 2 incorporates built-in safety precautions, aligning with Google’s Responsible AI principles. It features safety filters and integrates with a digital watermarking service to ensure responsible use.

Enterprise-ready capabilities

Imagen 2 on Vertex AI is designed to meet enterprise standards, offering reliability and governance akin to its predecessor. With new features such as high-quality image rendering, improved text rendering, logo generation, and safety measures, Imagen 2 aims to provide organisations with a comprehensive tool for creative image generation.

Leading companies like Snap, Shutterstock, and Canva have already embraced Imagen for creative purposes.

Chris Loy, Director of AI Services at Shutterstock, commented: “We exist to empower the world to tell their stories by bridging the gap between idea and execution.

“Variety is critical for the creative process, which is why we continue to integrate the latest and greatest technology into our image generator and editing features—as long as it is built on responsibly sourced data,”

Danny Wu, Head of AI at Canva, added: “We’re continuing to use generative AI to innovate the design process and augment imagination.

“With Imagen, our 170M+ monthly users can benefit from the image quality improvements to uplevel their content creation at scale.”

As Imagen 2 makes waves in the creative industry, organisations are encouraged to explore its potential. Google Cloud anticipates users will harness the new features to elevate their creative endeavours and build on the success achieved with Imagen.

(Photo by G on Unsplash)

See also: Microsoft unveils 2.7B parameter language model Phi-2

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Demis Hassabis, CEO and Co-Founder of Google DeepMind, introduced Gemini as a multimodal model that is capable of seamlessly understanding and combining various types of information, including text, code, audio, image, and video.

Gemini comes in three optimised versions: Ultra, Pro, and Nano. The Ultra model boasts state-of-the-art performance, surpassing human experts in language understanding and demonstrating unprecedented capabilities in tasks ranging from coding to multimodal benchmarks.

What sets Gemini apart is its native multimodality, eliminating the need for stitching together separate components for different modalities. This groundbreaking approach, fine-tuned through large-scale collaborative efforts across Google teams, positions Gemini as a flexible and efficient model capable of running on data centres to mobile devices.

One of Gemini’s standout features is its sophisticated multimodal reasoning, enabling it to extract insights from vast datasets with remarkable precision. The model’s prowess extends to understanding and generating high-quality code in popular programming languages.

However, as Google ventures into this new era of AI, responsibility and safety remain paramount. Gemini undergoes rigorous safety evaluations, including assessments for bias and toxicity. Google is actively collaborating with external experts to address potential blind spots and ensure the model’s ethical deployment.

Gemini 1.0 is now rolling out across various Google products – including the Bard chatbot – with plans for integration into Search, Ads, Chrome, and Duet AI. However, the Bard upgrade will not be released in Europe pending clearance from regulators.

Developers and enterprise customers can access Gemini Pro via the Gemini API in Google AI Studio or Google Cloud Vertex AI. Android developers will also be able to build with Gemini Nano via AICore, a new system capability available in Android 14.

(Image Credit: Google)

See also: AI & Big Data Expo: AI’s impact on decision-making in marketing

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The move is seen as a strategic way for Google to maintain its edge in the competitive AI industry and compete with OpenAI. By combining the talent and resources of both entities, Google DeepMind aims to accelerate AI advancements while maintaining ethical standards.

The new unit will be responsible for spearheading groundbreaking AI products and advancements, and it will work closely with other Google product areas to deliver AI research and products.

Google Research, the former parent division of Google Brain, will remain an independent division focused on “fundamental advances in computer science across areas such as algorithms and theory, privacy and security, quantum computing, health, climate and sustainability, and responsible AI.”

Demis Hassabis, CEO of DeepMind, believes that the consolidation of the two AI research labs will bring together world-class talent in AI with the computing power, infrastructure, and resources to create the next generation of AI breakthroughs and products boldly and responsibly.

Hassabis claims that the research accomplishments of Google Brain and DeepMind have formed the foundation of the current AI industry—ranging from deep reinforcement learning to transformers. The newly consolidated unit will build upon this foundation to create the next generation of groundbreaking AI products and advancements that will shape the world.

Over the years, Google and DeepMind have jointly developed several groundbreaking innovations. The duo’s achievements include AlphaGo – which famously beat professional human Go players – and AlphaFold, an exceptional tool that accurately predicts protein structures.

Other noteworthy achievements include word2vec, WaveNet, sequence-to-sequence models, distillation, deep reinforcement learning, and distributed systems and software frameworks like TensorFlow and JAX. These cutting-edge tools have proven highly effective for expressing, training, and deploying large-scale ML models.

Google’s acquisition of DeepMind for $500 million in 2014 paved the way for a fruitful collaboration between the two entities. With the consolidation of Google Brain and DeepMind into Google DeepMind, Google hopes to further advance its AI research and development capabilities.

Google’s chief scientist, Jeff Dean, will take on an elevated role as chief scientist for both Google Research and Google DeepMind. He has been tasked with setting the future direction of AI research at the company, as well as heading up the most critical and strategic technical projects related to AI, including a series of powerful multimodal AI models.

The creation of Google DeepMind underscores Google and parent company Alphabet’s commitment to furthering the pioneering research of both DeepMind and Google Brain. With the race to dominate the AI space becoming more intense, Google DeepMind is poised to accelerate AI advancements and create groundbreaking AI products and advancements that will shape the world.

(Image Credit: Google DeepMind)

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Ian Goodfellow is a renowned machine learning researcher. Goodfellow invented generative adversarial networks (GANs), developed a system for Google Maps that transcribes addresses from Street View car photos, and more.

In a departure note to his team at Apple, Goodfellow cited the company’s much-criticised lack of flexibility in its work policies.

Many companies were forced into supporting remote work during the pandemic and many have since decided to keep flexible working due to the recruitment advantages, mental/physical health benefits, lowering the impact of rocketing fuel costs, improved productivity, and reduced office space costs.

Apple planned for employees to work from the office on Mondays, Tuesdays, and Thursdays, starting this month. However, following backlash, on Tuesday the company put the plan on hold—officially citing rising Covid cases.

Goodfellow already decided to hand in his resignation and head to a company with more forward-looking, modern working policies.

The machine learning researcher had worked for Apple since 2019. Prior to Apple, Goodfellow had previously worked for Google as a senior research scientist.

Goodfellow is now reportedly returning to Google, albeit to its DeepMind subsidiary. Google is currently approving requests from most employees seeking to work from home.

More departures are expected from Apple if it proceeds with its return-to-office mandate.

“Everything happened with us working from home all day, and now we have to go back to the office, sit in traffic for two hours, and hire people to take care of kids at home,” a different former Apple employee told Bloomberg.

Every talented AI researcher like Goodfellow that leaves Apple is a potential win for Google and other companies.

(Photo by Viktor Forgacs on Unsplash)

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LinkedIn co-founder Reid Hoffman is joining Suleyman on the venture.

“Reid and I are excited to announce that we are co-founding a new company, Inflection AI,” wrote Suleyman in a statement.

“Inflection will be an AI-first consumer products company, incubated at Greylock, with all the advantages and expertise that come from being part of one of the most storied venture capital firms in the world.”

Dr Karén Simonyan, another former DeepMind AI expert, will serve as Inflection AI’s chief scientist and its third co-founder.

“Karén is one of the most accomplished deep learning leaders of his generation. He completed his PhD at Oxford, where he designed VGGNet and then sold his first company to DeepMind,” continued Suleyman.

“He created and led the deep learning scaling team and played a key role in such breakthroughs as AlphaZero, AlphaFold, WaveNet, and BigGAN.”

Inflection AI will focus on machine learning and natural language processing.

“Recent advances in artificial intelligence promise to fundamentally redefine human-machine interaction,” explains Suleyman.

“We will soon have the ability to relay our thoughts and ideas to computers using the same natural, conversational language we use to communicate with people. Over time these new language capabilities will revolutionise what it means to have a digital experience.”

Interest in natural language processing is surging. This month, Microsoft completed its $19.7 billion acquisition of Siri voice recognition engine creator Nuance.

Suleyman departed Google in January 2022 following an eight-year stint at the company.

While at Google, Suleyman was placed on administrative leave following bullying allegations. During a podcast, he said that he “really screwed up” and was “very sorry about the impact that caused people and the hurt people felt.”

Suleyman joined venture capital firm Greylock after leaving Google.

“There are few people who are as visionary, knowledgeable and connected across the vast artificial intelligence landscape as Mustafa,” wrote Hoffman, a Greylock partner, in a post at the time.

“Mustafa has spent years thinking about how technological advances impact society, and he cares deeply about the ethics and governance supporting new AI systems.”

Inflection AI was incubated by Greylock. Suleyman and Hoffman will both remain venture partners at the company.

Suleyman promises that more details about Inflection AI’s product plans will be provided over the coming months.

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