Nvidia has hit a new milestone, becoming the first company in the world to reach a market value of $5tn (£3.8tn). The US chip-maker has rapidly climbed from a niche graphics-chip manufacturer to an AI titan, as euphoria about the potential of artificial intelligence keeps driving demand for its chips and propelling its stock to record highs. The company reached a market value of $1tn for the first time in June 2023 and hit the $4tn valuation mark just three months ago . Shares in the chip-maker rose as much as 5.6% to more than $212 on Wednesday morning, boosted by investor optimism about Nvidia's sales in China, which has been a geopolitical flashpoint. The world's most valuable company - the biggest winner in the AI spending spree - has soared past its rivals in the technology sector.

That was the first thing I heard from one excited WWDC attendee as I waited to test Apple's Vision Pro mixed reality headset. That level is excitement is exactly what Apple is hoping for. Realistically, not everyone will be able to afford a $3,499 device. But if Apple can get mainstream consumers excited about the idea of spatial computing, then it'll be able to make a bigger splash when it inevitably unveils a more affordable follow-up. After spending thirty minutes with the Vision Pro, my reaction is more tempered than that excitable attendee.

Vanilla graph databases are pretty much everywhere thanks to the ever-growing graphs in production, flexible graph data models, and expressive query languages. Query engines assume that graphs in classical graph DBs are complete. Under the completeness assumption, we can build indexes, store the graphs in a variety of read/write-optimized formats and expect the DB would return what is there. But this assumption does not often hold in practice (we'd say, doesn't hold way too often). If we look at some prominent knowledge graphs (KGs): in Freebase, 93.8% of people have no place of birth and 78.5% have no nationality, about 68% of people do not have any profession, while in Wikidata, about 50% of artists have no date of birth, and only 0.4% of known buildings have information about height.

To accomplish a task like checking to see whether you locked the front door or retrieving a cell phone that's ringing in an upstairs bedroom, AI assistants of the future must learn to plan their route, navigate effectively, look around their physical environment, listen to what's happening around them, and build memories of the 3D space. These smarter assistants will require new advances in embodied AI, which seeks to teach machines to understand and interact with the complexities of the physical world as people do. Today, we're announcing several new milestones that introduce important capabilities to push the limits of embodied agents even further. The first audio-visual platform for embodied AI. With this new platform, researchers can train AI agents in 3D environments with highly realistic acoustics.

The quest to teach machines to understand human conversations has taken another big step forward with researchers achieving a new level of speech recognition for technology. Xuedong Huang, who leads Microsoft's Speech and Language Group, announced the new milestone in a blog post. Last year his team made headlines when it reached human parity on the "Switchboard conversational speech recognition task", meaning it had created technology that could recognize words in spoken conversation as well as professional human transcribers. Switchboard is a corpus of recorded telephone conversations that the speech research community has used for more than 20 years to benchmark speech recognition systems. The task involves the technology transcribing conversations between strangers who discuss various topics, such as sports and politics.

Beating expert poker players differs from past AI successes against human competitors in games such as Jeopardy and Go. Researchers behind a poker-playing AI system called DeepStack say it's the first algorithm to have ever beaten poker pros in heads-up no-limit Texas hold'em. The claim, if verified, would mark a major milestone in the development of artificial-intelligence systems. Beating expert poker players differs from past AI successes against human competitors in games such as Jeopardy and Go because each player's hand provides only an incomplete picture about the state of play and requires a program to navigate tactics, such as bluffing, based on asymmetrical information. DeepStack is the work of a collaboration between researchers at the University of Alberta and two Czech universities, who say in a new non-peer reviewed paper that it's the "first computer program to beat professional poker players in heads-up no-limit Texas hold'em".

Davis Jones is a communicator, academic, and content maker with 9 years of experience in content marketing, recruitment, product management and everything else that it takes to launch businesses. Located in Chicago and San Francisco.

Are you debating robots on Twitter? Davis Jones is a communicator, academic, and content maker with 9 years of experience in content marketing, recruitment, product management and everything else that it takes to launch businesses. Located in Chicago and San Francisco.