Today in AI

• News roundup

• AI research changing the world

• The AI tool we’re loving right now

• This week on the podcast

News roundup

The top stories in AI today.

FUTURE OF WORK

Jobs are changing — are you?

• Mentioning AI in Products May Reduce Consumer Trust

• Practical Tips to Build AI Literacy

• We May be Closer to AI Reading Our Minds

ALLIANCES

Who’s making moves in the AI game of thrones?

• Apple Chooses Google’s AI Chips Over Nvidia

• AI Search War: AI Firms Partner with Publishers

NEW LAUNCHES

The latest features & products in AI innovation.

• Zyphra Launches Small Open-Source Model

• Wix’s New AI Blogging Tools Will Write Your Article

GADGETS

Computers, phones, wearables & other AI gizmos.

• Nothing Phone (2a) Plus is an AI-Powered Smartphone

• AI Features to Expect in Apple’s iOS 18.1 Beta

F-AI-LS

Blunders, bloopers & face plants.

• Google’s AI Olympics Ad Sparks Backlash

GOVERNMENT

Press releases, regulation, defense & politics.

• U.S. Copyright Office Urges New Law to Combat Digital Replicas

• U.S. to Exempt Allies from Chip Restrictions

FINANCE & ECONOMICS

Economics + the movers & shakers on Wall Street.

• AMD’s Data Center Boom Overshadows Gaming Woes

• Microsoft Cloud Revenue Misses Expectations

• Samsung Q2 Profit Soars on Rising AI Chip Demand

AI research changing the world

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

How Google’s Transformer Will Take Us Beyond RNNs and CNNs

Google Brain's latest research presents a paradigm shift in language processing models. Their innovative architecture, named the Transformer, sidesteps the traditional reliance on recurrent or convolutional neural networks, opting instead for an attention-based system. Unlike its predecessors which process data sequentially, the Transformer parallelizes computations, drastically reducing training time and setting new benchmarks in translation quality.

The core of the Transformer's success lies in its ability to draw global dependencies between input and output, a feat made possible by its self-attention mechanism (which allows a model to weigh the importance of different parts of an input). This approach not only accelerates training – with experiments on machine translation tasks yielding superior results in less time – but also establishes new state-of-the-art performance.

The implications of the Transformer model are vast, offering a glimpse into a future where AI can process language with unprecedented efficiency and effectiveness. Its potential extends beyond just translation, to promising applications in parsing and other language tasks.

The AI tool we’re loving right now

The best way to get AI literate? Try the tools!

This week on the podcast

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