The ultimate guide to the state of today’s A.I.

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Fortune’s beautifully redesigned magazine hits newsstands this week. I urge you all to check it out, not least because the coverage package is devoted entirely to artificial intelligence. The issue serves as a concise field guide to the state of A.I. technology today.
Here’s a preview of what you’ll find inside.

• Why are some of the world’s most valuable corporations putting big money into efforts to create artificial general intelligence (or AGI), the kind of human-like, super-capable A.I. that exists only in Hollywood flicks and sci fi paperbacks? I explore that question through the prism of Microsoft’s $1 billion investment into San Francisco-based OpenAI. The answer, it turns out, has as much to do with the quest’s ancillary benefits—improved algorithms, better cloud computing capabilities and, just as importantly, branding— as it does with a desire to actually achieve the moonshot’s ultimate aim, something most A.I. researchers think won’t happen for decades, if at all.

• In a companion story, I examine recent breakthroughs in natural language processing and their impact on business. After years of A.I.’s language capabilities lagging progress in computer vision, the past eighteen months have seen a series of advances. What’s more, those new language models are making a faster leap out of the lab and into products used by billions than ever before. Could better language understanding be the key to unlocking more human-like A.I.? Some experts think so.

• My colleague Maria Aspan delves into A.I.’s use in hiring and human resources management, one of the hottest areas for the technology. A desire to move beyond human biases and widen the talent pool is driving adoption of machine learning-driven technologies in hiring. But, as Maria reports, the opaque nature of many of the models used by HR algorithms is raising new concerns that companies have simply swapped one kind of bias for another, more insidious, kind.

• Freelance reporter Jennifer Alsever looks at the crop of startups hoping to upend the pharmaceutical industry by using A.I. in drug discovery. In particular, Toronto-based Deep Genomics used machine learning to find a therapeutic candidate for the rare genetic disorder Wilson’s disease. There’s plenty of hope that companies like this will help slash drug development costs. But Eric Topol, the cardiologist and geneticist who has become an important voice of moderation amid all the hype surrounding A.I. in medicine, tells Jennifer the whole field right now is “long on promise and short on proof.”

• Hong Kong-based Eamon Barrett examines China’s national ambitions to become a world leader in artificial intelligence. Beijing’s strategic aims and surging funding for the technology have set alarm bells ringing in Washington. China also has access to vast pools of data on its own citizens. However, Jeffrey Ding, a researcher at Oxford’s Future of Humanity Institute who studies China’s A.I. strategy, tells Barrett that “the U.S. is still far ahead,” continuing to hold the lead in both algorithms and the specialized computing hardware needed to run A.I. systems.

• Barrett’s story is well worth checking out for its discussion of ByteDance, the company behind the wildly popular TikTok. Machine learning-driven recommendations lie at the heart of TikTok’s success, Barrett writes, but Chinese officials haven’t exactly embraced ByteDance as a standard bearer for homegrown A.I. expertise. Why? Apparently, Chinese Communist Party officials might consider the video sharing service too frivolous. Given how important social networks such as Facebook and Twitter have become to political discourse globally, Beijing may be overlooking a potent strategic asset.

Read the full package here, and keep reading for a quick round-up of the week’s other A.I. news.

Jeremy Kahn
@jeremyakahn
[email protected]

This story was originally featured on Fortune.com