The challenges AI faces are shifting from what the field can do to what it should do
Mar 3, 2021 - TechnologyAutomation isn't destroying warehouse work, but it is shaping it in challenging ways.
Feb 27, 2021 - TechnologyBut without changes to tax regulations and training, human workers will lose ground over time
Oct 31, 2020 - TechnologyVirtual agents could augment human workers in online services at a time of mass unemployment
May 2, 2020 - TechnologyIt deserves more attention than it's getting in the 2020 presidential race.
Dec 8, 2019 - Economy & BusinessNew research shows that no one is immune.
Nov 20, 2019 - TechnologyIllustration: Annelise Capossela/Axios
Artificial intelligence is breaking into the doctor's office, with new models that can transcribe, analyze and even offer predictions based on written notes and conversations between physicians and their patients.
Why it matters: AI models can increasingly be trained on what we tell our doctors, now that they're starting to understand our written notes and even our conversations. That will open up new possibilities for care — and new concerns about privacy.
Illustration: Sarah Grillo/Axios
A new list of the top 100 private AI companies shows that health is driving investment in the industry.
Why it matters: COVID-19 has shown the power and potential of AI applications for health, and the growth of the field will continue long after the pandemic has finally ended.
Photo: Screenshot of Bryan Walsh playing the Emojify game
New AI tools purport to be able to identify human emotion in images and speech patterns.
Why it matters: Prompted in part by the push of the pandemic, tech companies have been advertising emotion recognition programs, but experts warn they may not work — and may be misused.
Illustration: Sarah Grillo/Axios
Sarcos Robotics, a Salt Lake City-based developer of robotic exoskeletons, agreed to go public at a $1.3 billion implied valuation via acquisition by Rotor Acquisition (NYSE: ROT), a SPAC led by Wall Street vet Brian Finn.
Why it matters: Expect this one to get some special scrutiny from the SEC. Finn's venture capital firm, Rotor Capital, last year led a Series C investment in Sarcos and also participated in a CES product unveiling. And Finn seems aware of the potential pitfalls, mentioning the existing relationship early in today's investor presentation.
Illustration: Annelise Capossela/Axios
A startup is employing AI to streamline and perfect manufacturing.
Why it matters: As valuable as machine learning has been in software, the next phase could be even more disruptive: bringing AI to the often messy process of making things.
Illustration: Aïda Amer/Axios
The ACLU will be seeking information about how the government is using artificial intelligence in national security, Axios has learned.
Why it matters: The development of AI has major implications for security, surveillance, and justice. The ACLU's request may help shed some light on the government's often opaque applications of AI.
A tractor in a John Deere factory. Photo courtesy of John Deere
John Deere is planning on introducing 5G technology into its factories, including in Iowa.
Why it matters: 5G is expected to streamline operations, ranging from automated parts delivery to even how employees move in the assembly line.
Illustration: Aïda Amer/Axios
AI companies are generating synthetic data to train machine learning systems.
Why it matters: Using computer-generated data to train AI systems can help address privacy concerns and cut down on bias while meeting the needs of models that operate in highly specific environments.
Illustration: Eniola Odetunde/Axios
Software bots are getting smarter and more capable, enabling them to automate much of the work carried out in offices.
Why it matters: Bots can make digital work more efficient by taking on onerous and repetitive white-collar tasks, but the better they get, the more competition they pose to skilled workers who might have thought themselves exempt from the job-disrupting effects of automation.
Illustration: Sarah Grillo/Axios
New research from a major AI company offers insight into how neural networks are able to "see."
Why it matters: Reliable computer vision is a cornerstone for AI applications like self-driving cars, but the effectiveness of neural nets in recognizing images is only matched by their impenetrability. The new research allows scientists to peer into the black box of computer vision, with implications for reducing bias and errors.