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What’s happening in AI right now

The AI arms race heats up: cloud giants, chip startups, and sustainability concerns

The cloud computing landscape is undergoing a seismic shift, with artificial intelligence emerging as the new battleground for market dominance. Recent data shows that the global cloud infrastructure market grew by a staggering 19% year-over-year in Q2 2024, reaching $78.2 billion. This surge is largely attributed to the growing demand for AI services, with industry giants Amazon Web Services, Microsoft Azure, and Google Cloud capturing 63% of total spending.

The cloud-AI nexus

As enterprises increasingly adopt AI technologies, cloud providers are scrambling to differentiate themselves through specialized offerings. This race to the top is reshaping the industry, with providers investing heavily in AI infrastructure and partnerships in order to capture a larger slice of the expanding pie.

Skepticism amidst the hype

Despite this frenzied investment and optimism surrounding AI, voices of caution are emerging. Prominent activist investment firm Elliott Management has expressed skepticism about the current valuation of AI chip giant Nvidia and the overall hype surrounding AI technologies. The firm argues that many AI applications may be economically unfeasible, or environmentally unsustainable.

In addition, this rapid expansion of AI infrastructure is not without its own challenges, particularly in terms of environmental impact. The recent controversy surrounding Elon Musk's xAI data center in Memphis highlights the growing tension between technological advancement and sustainability. The facility's massive energy and water requirements have raised concerns among local residents and environmental activists, underscoring the need for more sustainable approaches to AI infrastructure development.

Hardware innovation may lead the charge in addressing these concerns with purpose-built architectures. Startups like EdgeCortix are making waves with energy-efficient AI chips designed for edge computing. The Japanese company's software-first approach and novel hardware solutions, such as the Dynamic Neural Accelerator, are addressing critical challenges in AI processing efficiency. With applications ranging from defense to smart cities, EdgeCortix exemplifies the potential for disruptive innovation in the AI chip market.

Geopolitical implications

On the flipside, the AI infrastructure and computing race extends beyond mere corporate competition and environmental challenges, evolving into a matter of national priority. Governments worldwide are implementing policies to support domestic AI industries and forming strategic alliances to maintain technological sovereignty. This shift towards "AI nationalism" is, in effect, reshaping international relations, fostering new partnerships while intensifying existing rivalries.

The AI arms race is reshaping the technology landscape, driving innovation while raising important questions about sustainability, ethics, and economic feasibility. As we move forward, it will be crucial for businesses, policymakers, and society at large to strike a balance between technological progress and responsible development.

News roundup

The top stories in AI today.

FUTURE OF WORK

Jobs are changing — are you?

• How Mainstream AI Adoption Will Be Driven By Small Wins

• Why Enterprise CIOs Must Prepare for Microsoft Gen AI

• AI and the Metaverse are Ushering in a New Era for Film

NEW LAUNCHES

The latest features & products in AI innovation.

• New AI Tool Enables Real-Time Face-Swapping on Webcams

• Shaped AI Unveils Powerful Semantic Search Platform for Marketplaces

• Y Combinator Company Launches AI-Powered ETL Solution

• Google’s AI Research Assistant Automates Complex Research Gathering

• MIT Unveils Comprehensive AI Risk Database with 700+ Threats

GADGETS

Computers, phones, wearables & other AI gizmos.

• OnePlus Unveils 3 AI Features to Boost Smartphone Productivity

• AI-Enabled PCs Are Moving Like Hotcakes

• Google’s Pixel 9 Lineup Lets You Search Across Your Screenshots

• Apple’s AI Ambitions Face Hurdles as Competitors Surge Ahead

• How Google is Making All the Features in Your Phone Revolve Around Gemini AI

• Acer Swift Go 14 Brings AI Power to Students for $799

• AI is About to Enable an iPhone Moment for Robots

AI MODELS

Training, infrastructure, and research

• MIT Researchers Unveil AI Framework to Detect Anomalies in Time Series Data

• xAI Releases Grok-2 and Grok-2 Mini with Advanced AI Features

• AI’s Energy Demands Spark Waste-to-Power Innovation

• New AI Model ‘ActFound’ Accelerates Drug Discovery

• Google’s Free Gemini AI Outperforms Paid Version in Key Tests

GOVERNMENT

Press releases, regulation, defense & politics.

• Artists Gain Ground in AI Copyright Lawsuits

• EU Scrutiny Intensifies as X Faces AI Data Usage Restrictions

• AI Race Intensifies as Nations Vie for Technological Supremacy

FINANCE & ECONOMICS

Economics + the movers & shakers on Wall Street.

• AI Propels Norway's Wealth Fund to $138 Billion Profit

• Analysts Believe Apple Intelligence Features Likely to Remain Free (for Now)

• HBR: Remote Workers Stand to Gain (and Lose) the Most from Generative AI

• Rising Demand for AI is Creating a Windfall for Cloud Service Providers

AI research changing the world

The latest breakthroughs and most pivotal papers — broken down in language anyone can understand.

DeepMind’s AI solves nature’s origami by predicting protein structures with atomic precision

The ability to rapidly decipher the 3D structure of any protein, unlocking secrets of diseases and accelerating drug discovery from years to days, has long been a holy grail in biotechnology. This ambitious goal is now a reality, thanks to DeepMind's AlphaFold, as reported in Nature. This AI breakthrough solves the 50-year-old "protein folding problem," achieving near-experimental accuracy in predicting protein structures from amino acid sequences. AlphaFold's median backbone accuracy of 0.96 Å RMSD outperforms previous methods by a factor of three, approaching the precision of experimental techniques.

This leap forward has profound implications across biotechnology, potentially revolutionizing drug development, disease research, and synthetic biology. By democratizing access to protein structure information, AlphaFold could level the playing field in biotech research, allowing smaller labs to contribute significantly to the field. However, challenges remain, such as predicting structures of protein complexes and capturing protein dynamics. As we stand on the brink of a new era in structural biology, one can't help but wonder: How will this AI-driven approach reshape the landscape of medical research and personalized medicine?

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