With Uber and Lyft recently announcing large quarterly losses, it's clear they have to make serious changes to reach profitability and even stay in business. One potential path to survival is to replace their biggest operational expense, the human driver, with a fully autonomous fleet. Autonomous vehicles (AV) will save Uber and Lyft (and other companies) in addition to making our roads safer. According to the Association for Safe International Road Travel, globally, "Nearly 1.25 million people die in road crashes each year, on average 3,287 deaths a day." This is equivalent to wiping out the entire population of San Jose every year.

For decades, the necessary technology tools have been available to generate more and more data, as it relates to various processes in construction such as project management, scheduling, job costing, and more. The challenge is, with the influx of data, in the last few years, it has become more difficult to use that information well. "It hasn't been centrally stored and there hasn't been computing capability, even if the information was centrally stored," explains Dan Patterson, chief design officer, InEight, www.ineight.com, "That information is very loosely structured; it is not a uniform format. It is very difficult for computers to then mine that historical information."

Throughout the centuries, humans have made tremendous leaps forward in the way we build, interact, and communicate with each other and the world. More recently, we've shifted self-execute industrialization to the age of information. We now have a seemingly unlimited amount of knowledge available at our fingertips. Technological advances are now accelerating faster than ever before. As technology continues to evolve, we can expect it to impact all aspects of our lives and society as a whole.

Across all industries, technologies like automation, artificial intelligence (AI) and machine learning are improving processes, increasing efficiency and aiding decision-makers. As these tools evolve and become even more advanced, there will be a greater number of potential applications across more fields, including the nonprofit sector. We asked a panel of Forbes Nonprofit Council members how they foresee this cutting-edge technology impacting their operations in the next five years. Here are their predictions for a nonprofit world powered by automation and AI. As online platforms grow in sophistication, it will be easier and easier to automate donor communications that feel personalized and authentic.

There is, alas, no such thing as a free lunch. This simple and obvious truth is invariably forgotten whenever irrational exuberance teams up with digital technology in the latest quest to "change the world". A case in point was the bitcoin frenzy, where one could apparently become insanely rich by "mining" for the elusive coins. All you needed was to get a computer to solve a complicated mathematical puzzle and – lo! – you could earn one bitcoin, which at the height of the frenzy was worth $19,783.06. All you had to do was buy a mining kit (or three) from Amazon, plug it in and become part of the crypto future. The only problem was that mining became progressively more difficult the closer we got to the maximum number of bitcoins set by the scheme and so more and more computing power was required.

Those are some of the adjectives being used to describe "Death Stranding," the much-anticipated video game from famed game designer Hideo Kojima ("Metal Gear Solid") out now for Sony's PlayStation 4. Since Kojima revealed the game was under development three years ago, with a trailer showing Norman Reedus ("The Walking Dead") as the main character in a sci-fi setting, expectations have been high – and speculation has spiked – about the prospects for "Death Stranding," which is currently only available on the PS4. A PC version is in the works for summer 2020. A measurement of demand for the game comes from Nielsen, which asked 10,000 gamers, ages 7 to 54, to rank their interest in games set to be released before and during the holiday season. Nintendo Switch:'Super Smash Bros. Ultimate' is officially the best-selling game in the series While "The Legend of Zelda: Link's Awakening" (Score: 99), "Call of Duty: Modern Warfare" (98), "Luigi's Mansion 3" (96) and "Borderlands 3" (95) led the way, "Death Stranding" was close behind with a score of 94, ahead of well-known titles such as "Gears 5" (88), "FIFA 20" (87), and "Star Wars Jedi: Fallen Order" (82). But "Death Stranding" is a different game and has arrived as sort of a Rorschach test for game critics.

Would you train a neural network with random data? Moreover, are massive neural networks just lookup tables or do they truly learn something? Today's episode is about memorisation and generalisation in deep learning, with Stanislaw Jastrzębski. Stan works as post-doc at New York University. I have asked Stan a few questions I was looking answers for a long time.

As is the case with many technological developments, 'deepfakes' -- videos in which someone who did not originally appear in the clip is rendered into it using artificial intelligence (AI) -- largely started in the world of pornography. Viewers, should they so desire, can now watch videos of their favourite musicians and film stars "in action," although that celebrity was never in that video. In these cases, increasingly sophisticated tools are used to put the musicians and film stars' faces onto pre-existing pornographic videos. There can obviously be a sinister, non-celebrity side to this too. The recent Sam Bourne novel, To Kill The Truth, features a protagonist Maggie Costello who appears in such a video as part of a cruel plot to undermine her.

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Among the consequences of big data is a wealth of relevant minutiae that can be used to train machine learning and other models. That often translates into processing-intensive steps required to train models to perform a specific task. In response, technology startups and government-funded university researchers are promoting a new machine intelligence approach called "lifelong" machine learning as a way to accelerate model training. The latest entrant is Neurala, which this week took the wraps off its lifelong deep neural network technology. The Boston-based startup claims its AI approach can slash training time from upwards of 15 hours to as little as 20 seconds by utilizing "incremental learning."