Musk has some history in the AI space. He co-founded OpenAI in 2015 alongside the current CEO, Sam Altman, and others. However, he parted ways with the company in 2018 due to disagreements over its priorities and direction, specifically OpenAI’s move away from open-source AI models and towards proprietary, closed models that they sell access to. Musk became one of OpenAI’s most vocal critics after that.

Fast-forward to July of last year, and Musk formally re-entered the generative AI arena with the announcement of xAI. Since then, the company has been aggressively working on building advanced AI systems that it claims will be “truthful, competent, and maximally beneficial for all of humanity.”

One of xAI’s first offerings is Grok, an edgier version of OpenAI’s ChatGPT. It is currently available only to X Premium subscribers. According to benchmarks shared by xAI, Grok-1 outperformed models like Llama-2-70B and GPT-3.5 but still lagged behind Anthropic’s Claude3, OpenAI’s GPT-4, Google’s Gemini, and Meta’s open-source Llama 3.

This latest funding round saw investments from big names, including Andreessen Horowitz, Sequoia Capital, and even Saudi Arabian Prince Al Waleed bin Talal. Last year, filings showed xAI was looking to raise up to $1 billion in equity investments, and just a few months ago, reports surfaced that they had increased their target to a whopping $6 billion—a figure Musk initially denied.

Developing the hardware capable of powering AI at this sophisticated level is no small feat and certainly is costly. Just last week, a report revealed that xAI would need an astonishing 100,000 of Nvidia’s current H100 chips for a supercomputer to power an upgraded version of Grok, each costing between $30,000 to $40,000.

Musk has ambitious plans for these powerful resources. He reportedly informed investors that the goal is to launch this new data centre by fall 2025, marking a significant step forward in xAI’s technological capabilities.

Continuing this AI race for chips, talent, and technology will be expensive. Big tech firms have already invested billions into AI startups like Anthropic, on top of what companies like Google, Apple, Amazon, Microsoft, and Meta are investing in their own AI projects.

Microsoft, in particular, has struck a multi-billion-dollar partnership with OpenAI, whose CEO, Sam Altman, is reportedly pursuing trillions more to revamp the global chip industry.

Tesla will continue to hire AI engineers for self-driving cars, even though Elon Musk has previously stated a preference for not bringing most of those AI and robotics capabilities in-house, to allow greater focus on external ventures. He talks about breaking free from the traditional constraints of auto manufacturing as part of a broader agenda.

At the same time, Tesla is gearing up for its annual meeting on June 13, where shareholders will start voting on whether to reinstate Musk’s record-breaking $56 billion pay package, a significant development given Musk’s substantial influence across various tech sectors.

Over the years, Musk has made substantial contributions to AI research, and now his venture, xAI, is at the forefront of AI developments. It will be fascinating to watch how these efforts unfold and see which of xAI’s contributions will secure a place in the annals of AI history during this rapid evolution.

See also: Elon Musk’s xAI open-sources Grok

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 other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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Gemini 1.5 Flash is a new addition to Google’s family of models, designed to be faster and more efficient to serve at scale. While lighter-weight than the 1.5 Pro, it retains the ability for multimodal reasoning across vast amounts of information and features the breakthrough long context window of one million tokens.

“1.5 Flash excels at summarisation, chat applications, image and video captioning, data extraction from long documents and tables, and more,” explained Demis Hassabis, CEO of Google DeepMind. “This is because it’s been trained by 1.5 Pro through a process called ‘distillation,’ where the most essential knowledge and skills from a larger model are transferred to a smaller, more efficient model.”

Meanwhile, Google has significantly improved the capabilities of its Gemini 1.5 Pro model, extending its context window to a groundbreaking two million tokens. Enhancements have been made to its code generation, logical reasoning, multi-turn conversation, and audio and image understanding capabilities.

The company has also integrated Gemini 1.5 Pro into Google products, including the Gemini Advanced and Workspace apps. Additionally, Gemini Nano now understands multimodal inputs, expanding beyond text-only to include images.

Google announced its next generation of open models, Gemma 2, designed for breakthrough performance and efficiency. The Gemma family is also expanding with PaliGemma, the company’s first vision-language model inspired by PaLI-3.

Finally, Google shared progress on Project Astra (advanced seeing and talking responsive agent), its vision for the future of AI assistants. The company has developed prototype agents that can process information faster, understand context better, and respond quickly in conversation.

“We’ve always wanted to build a universal agent that will be useful in everyday life. Project Astra, shows multimodal understanding and real-time conversational capabilities,” explained Google CEO Sundar Pichai.

“With technology like this, it’s easy to envision a future where people could have an expert AI assistant by their side, through a phone or glasses.”

Google says that some of these capabilities will be coming to its products later this year. Developers can find all of the Gemini-related announcements they need here.

See also: GPT-4o delivers human-like AI interaction with text, audio, and vision integration

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 other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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The ML Olympiad – organised by groups including ML GDE, TFUG, and other ML communities – aims to provide developers with hands-on opportunities to learn and practice machine learning skills by tackling real-world challenges.

Over the previous two rounds, an impressive 605 teams participated across 32 competitions, generating 105 discussions and 170 notebooks.

This year’s lineup includes challenges spanning areas like healthcare, sustainability, natural language processing (NLP), computer vision, and more. Competitions are hosted by expert groups and developers from around the world.
Here are this year’s challenges:

• Smoking Detection in Patients

Hosted by Rishiraj Acharya (AI/ML GDE) in collaboration with TFUG Kolkata, this competition tasks participants with predicting smoking status using bio-signal ML models.

• TurtleVision Challenge

Organised by Anas Lahdhiri under MLAct, this challenge calls for the development of a classification model to differentiate between jellyfish and plastic pollution in ocean imagery.

• Detect Hallucinations in LLMs

Luca Massaron (AI/ML GDE) presents a unique challenge of identifying hallucinations in answers provided by a Mistral 7B instruct model.

• ZeroWasteEats

Anushka Raj, alongside TFUG Hajipur, seeks ML solutions to mitigate food wastage, a critical concern in today’s world.

• Predicting Wellness

Hosted by Ankit Kumar Verma and TFUG Prayagraj, this competition involves predicting the percentage of body fat in men using multiple regression methods.

• Offbeats Edition

Ayush Morbar from Offbeats Byte Labs invites participants to build a regression model to predict the age of crabs.

• Nashik Weather

TFUG Nashik challenges participants to forecast the weather condition in Nashik, India, leveraging machine learning techniques.

• Predicting Earthquake Damage

Usha Rengaraju presents a task of predicting the level of damage to buildings caused by earthquakes, based on various factors.

• Forecasting Bangladesh’s Weather

TFUG Bangladesh (Dhaka) aims to predict rainfall, average temperature, and rainy days for a particular day in Bangladesh.

• CO2 Emissions Prediction Challenge

Md Shahriar Azad Evan and Shuvro Pal from TFUG North Bengal seek to predict CO2 emissions per capita for 2030 using global development indicators.

• AI & ML Malaysia

Kuan Hoong (AI/ML GDE) challenges participants to predict loan approval status, addressing a crucial aspect of financial inclusion.

• Sustainable Urban Living

Ashwin Raj and BeyondML task participants with predicting the habitability score of properties, promoting sustainable urban development.

• Toxic Language (PTBR) Detection

Hosted in Brazilian Portuguese, this challenge by Mikaeri Ohana, Pedro Gengo, and Vinicius F. Caridá (AI/ML GDE) involves classifying toxic tweets.

• Improving Disaster Response

Yara Armel Desire of TFUG Abidjan invites participants to predict humanitarian aid contributions in response to disasters worldwide.

• Urban Traffic Density

Kartikey Rawat from TFUG Durg calls for the development of predictive models to estimate traffic density in urban areas.

• Know Your Customer Opinion

TFUG Surabaya presents a challenge of classifying customer opinions into Likert scale categories.

• Forecasting India’s Weather

Mohammed Moinuddin and TFUG Hyderabad task participants with predicting temperatures for specific months in India.

• Classification Champ

Hosted by TFUG Bhopal, this competition involves developing classification models to predict tumour malignancy.

• AI-Powered Job Description Generator

Akaash Tripathi from TFUG Ghaziabad challenges participants to build a system that automatically generates job descriptions using Generative AI and chatbot interface.

• Machine Translation French-Wolof

GalsenAI presents a challenge of accurately translating French sentences into Wolof, offering a platform to enhance language translation capabilities.

• Water Mapping using Satellite Imagery

Taha Bouhsine of ML Nomads tasks participants with water mapping using satellite imagery for dam drought detection.

Google is supporting each community host this round through its Google for Developers program.

Participants are encouraged to search for “ML Olympiad” on Kaggle, follow #MLOlympiad on social media, and get involved in the competitions that most interest them.

With such a diverse array of real-world machine learning challenges, the ML Olympiad represents an excellent opportunity for developers to put their skills to the test and gain valuable experience.

(Image Credit: Google)

See also: Microsoft: China plans to disrupt elections with AI-generated disinformation

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Linwei Ding, a 38-year-old Chinese national, was arrested on Wednesday in Newark, California, and faces four counts of federal trade secret theft, each punishable by up to 10 years in prison.

The indictment alleges that Ding, who was hired by Google in 2019 to develop software for the company’s supercomputing data centres, began transferring sensitive trade secrets and confidential information to his personal Google Cloud account in 2021.

“Ding continued periodic uploads until May 2, 2023, by which time Ding allegedly uploaded more than 500 unique files containing confidential information,” said the US Department of Justice in a statement.

Prosecutors claim that after stealing the trade secrets, Ding was offered a chief technology officer position at a startup AI company in China and participated in investor meetings for that firm. Additionally, Ding is alleged to have founded and served as CEO of a China-based startup focused on training AI models using supercomputing chips.

“Today’s charges are the latest illustration of the lengths affiliates of companies based in the People’s Republic of China are willing to go to steal American innovation,” said FBI Director Christopher Wray.

“The theft of innovative technology and trade secrets from American companies can cost jobs and have devastating economic and national security consequences.”

If convicted on all counts, Ding faces a maximum penalty of 40 years in prison and a fine of up to $1 million.

The case underscores the ongoing tensions between the US and China over intellectual property theft and the race to dominate emerging technologies like AI.

(Photo by Towfiqu Barbhuiya on Unsplash)

See also: OpenAI: Musk wanted us to merge with Tesla or take ‘full control’

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The post Google engineer stole AI tech for Chinese firms appeared first on AI News.

The controversy arose as users on social media platforms flooded feeds with examples of Gemini generating pictures depicting racially-diverse Nazis, black medieval English kings, and other improbable scenarios.

Google Gemini Image generation model receives criticism for being 'Woke'.

Gemini generated diverse images for historically specific prompts, sparking debates on accuracy versus inclusivity. pic.twitter.com/YKTt2YY265

— Darosham (@Darosham_) February 22, 2024

Meanwhile, critics also pointed out Gemini’s refusal to depict Caucasians, churches in San Francisco out of respect for indigenous sensitivities, and sensitive historical events like Tiananmen Square in 1989.

In response to the backlash, Jack Krawczyk, the product lead for Google’s Gemini Experiences, acknowledged the issue and pledged to rectify it. Krawczyk took to social media platform X to reassure users:

For now, Google says it is pausing the image generation of people:

While acknowledging the need to address diversity in AI-generated content, some argue that Google’s response has been an overcorrection.

Marc Andreessen, the co-founder of Netscape and a16z, recently created an “outrageously safe” parody AI model called Goody-2 LLM that refuses to answer questions deemed problematic. Andreessen warns of a broader trend towards censorship and bias in commercial AI systems, emphasising the potential consequences of such developments.

Addressing the broader implications, experts highlight the centralisation of AI models under a few major corporations and advocate for the development of open-source AI models to promote diversity and mitigate bias.

Yann LeCun, Meta’s chief AI scientist, has stressed the importance of fostering a diverse ecosystem of AI models akin to the need for a free and diverse press:

Bindu Reddy, CEO of Abacus.AI, has similar concerns about the concentration of power without a healthy ecosystem of open-source models:

As discussions around the ethical and practical implications of AI continue, the need for transparent and inclusive AI development frameworks becomes increasingly apparent.

(Photo by Matt Artz on Unsplash)

See also: Reddit is reportedly selling data for AI training

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The post Google pledges to fix Gemini’s inaccurate and biased image generation appeared first on AI News.

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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The post Google launches Gemini 1.5 with ‘experimental’ 1M token context appeared first on AI News.

Unveiled in December, Bard was touted as a competitor to chatbots like ChatGPT but failed to impress in demos. Google staff even called the launch “botched” and slammed CEO Sundar Pichai.

Now rebranded as Gemini, Google says it represents the company’s “most capable family of models” for natural conversations. Two experiences are being launched: Gemini Advanced and a mobile app.

Gemini Advanced grants access to Ultra 1.0, billed by Google as its “largest and most capable state-of-the-art AI model.” In blind evaluations, third-party raters preferred Gemini Advanced with Ultra 1.0 over alternatives in complex tasks like coding, logical reasoning, and creative collaboration.  

The AI can serve as a tutor by creating personalised lessons and quizzes. Developers are aided for trickier coding problems. Gemini Advanced is designed to spark ideas and strategise ways that creators can grow their audiences.

Google plans to expand Gemini Advanced’s capabilities over time with exclusive features like expanded multimodal interactions, interactive coding, deeper data analysis, and more. The service already supports over 150 countries in English and will add more languages soon.  

Access to Gemini Advanced is granted through a new $19.99 (£18.99) per month Google One AI Premium Plan, including a free two-month trial. Subscribers get the latest Google AI advancements plus 2TB of storage from the existing Premium plan.  

Google claims Gemini Advanced underwent extensive trust and safety checks before its launch, including external reviews, to mitigate issues around unsafe content and bias. More details are available in an updated technical report (PDF).

Lastly, Google launched new mobile apps on Android and iOS to access basic Gemini features on-the-go. Users can ask for help with images, tasks, and more while out-and-about. Over time, the plan is for Gemini to become a true personal AI assistant.

The Gemini mobile apps are now available in the US as a dedicated app on Android and in the Google app on iOS, supporting English conversations initially. Next week, the apps expand to Japan and Korea, followed by more countries and languages thereafter.

(Image Credit: Google)

See also: DeepMind framework offers breakthrough in LLMs’ reasoning

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The post Google launches Gemini to replace Bard chatbot appeared first on AI News.

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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The post DeepMind framework offers breakthrough in LLMs’ reasoning appeared first on AI News.

The 33-acre site in Waltham Cross, Hertfordshire will bring much-needed compute capacity to businesses, supporting AI innovation and ensuring reliable digital services for Google Cloud customers and general consumers relying on products like Search, Maps, and YouTube.

Ruth Porat, Alphabet’s president and chief financial officer, said the data centre “represents our latest investment in the UK and the wider digital economy.” She added that it builds on previous investments like Saint Giles and Kings Cross offices, a multi-year research deal with Cambridge, and the Grace Hopper subsea cable connecting the UK with the US and Spain.

Porat said the facility will “help meet growing demand for our AI and cloud services and bring crucial compute capacity to businesses across the UK while creating construction and technical jobs for the local community.”

As a pioneer in computing infrastructure, Google runs some of the most efficient data centres in the world and has committed to powering them entirely on carbon-free energy around the clock by 2030.

Last year, Google signed a deal with ENGIE for offshore wind energy from Scotland’s Moray West farm which will provide 100MW of energy and put UK operations on track for 90 percent clean energy by 2025.

The new data centre will recover heat for local homes and businesses while also deploying an air-cooling system.

Porat called the new data centre the “latest in a series of investments that support Brits and the wider economy” and evidence of its “continued commitment to the UK.” Other investments include $1 billion for its Central Saint Giles office space, developing the one million sq ft King’s Cross campus, and an Accessibility Discovery Centre spurring accessible technology.

Beyond offices, data centres, and subsea cables, Google has also provided digital skills training for over a million Brits and expanded its AI-focussed Digital Garage curriculum to capitalise on demand for the technology.  

Google’s announcement follows Microsoft confirming a £2.5 billion data centre in the UK last November after overcoming regulatory hurdles for its £55 billion Activision Blizzard acquisition.

“This is the single largest investment in its 40-year history in the country which will see Microsoft grow its UK AI infrastructure across sites in London and Cardiff and potential expansion into northern England, helping to meet the exploding demand for efficient, scalable, and sustainable AI specific compute power,” explained HM Treasury.

“Data centres process, host, and store the massive amounts of digital information that is critical for developing AI models.”

Microsoft is supplying its UK data centre with more than 20,000 advanced GPUs for machine learning and the development of new AI models.

Chancellor of the Exchequer Jeremy Hunt said: “The UK is the tech hub of Europe with an ecosystem worth more than that of Germany and France combined – and this investment is another vote of confidence in us as a science superpower.”

(Image Credit: Google)

See also: DeepMind AlphaGeometry solves complex geometry problems

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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

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 and Cyber Security & Cloud Expo.

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