Holographic display prototype used in the experiments

By  Derya Ozdemir

Not only can this technique run on a smartphone but it also needs less than 1 megabyte of memory.

Virtual reality has been around for decades, and every year, headlines all over the internet announce it to be the next big thing. However, those predictions are yet to become a reality, and VR technologies are far from being widespread. While there are many reasons for that, VR making users feel sick is definitely one of the culprits.

Better 3D visualization could help with that, and now, MIT researchers have developed a new way to produce holograms thanks to a deep learning-based method that works so efficiently that cuts down the computational power need in an instant, according to a press release by the university.

A hologram is an image that resembles a 2D window looking onto a 3D scene, and this 60-year-old technology remade for the digital world can deliver an outstanding image of the 3D world around us.

“People previously thought that with existing consumer-grade hardware, it was impossible to do real-time 3D holography computations,” explains Liang Shi, the study’s lead author and a Ph.D. student in MIT’s Department of Electrical Engineering and Computer Science. “It’s often been said that commercially available holographic displays will be around in 10 years, yet this statement has been around for decades.”

By Kyle Wiggers

By 2025, more than 75% of venture capital and early-stage investor executive reviews will be informed by AI and data analytics. In other words, AI might determine whether a company makes it to a human evaluation at all, de-emphasizing the importance of pitch decks and financials. That’s according to a new whitepaper by Gartner, which predicts that in the next four years, the AI- and data-science-equipped investor will become commonplace.

Increased advanced analytics capabilities are shifting the early-stage venture investing strategy away from “gut feel” and qualitative decision-making to a “platform-based” quantitative process, according to Gartner senior research director Patrick Stakenas. Stakenas says data gathered from sources like LinkedIn, PitchBook, Crunchbase, and Owler, along with third-party data marketplaces, will be leveraged alongside diverse past and current investments.

“This data is increasingly being used to build sophisticated models that can better determine the viability, strategy, and potential outcome of an investment in a short amount of time. Questions such as when to invest, where to invest, and how much to invest are becoming almost automated,” Stakenas said. “The personality traits and work patterns required for success will be quantified in the same manner that the product and its use in the market, market size, and financial details are currently measured. AI tools will be used to determine how likely a leadership team is to succeed based on employment history, field expertise, and previous business success.”