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.

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.

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.

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.

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.

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.

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.

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.

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.

Natural Language Processing is one of AI's hottest fields, yielding both practical products in the marketplace and bleeding-edge research.

The big picture: A virtual assistant that you can truly converse with — which depends on highly accurate speech and text recognition — is still beyond the horizon, but the field is making real progress.