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I recently participated in a regional workshop of government, education, business, and social leaders where we were trying to ascertain and assess 1) the certainty of national trends and 2) the impact of those trends on the region. We reviewed many trends, including the growth of green jobs, growth in the Hispanic community, the decline in water quality and availability, the increasingly older population, declining enrollment trends, remote medicine, Electric Vehicle (EV) infrastructure. I was particularly interested in the trend "Growing Artificial Intelligence (AI) Industry." "The AI industry is expanding rapidly, and two metro areas have become important federal research and contracting centers for AI research. However, according to a 2021 Brooking study, "these two metro areas exhibit below-average commercialization activities in terms of per capita AI companies, job postings, and job profiles," suggesting an opportunity to use this capability to help spark job growth."
Part of the challenge of "AI" is we keep raising the bar on what it means for something to be a machine intelligence. Early machine learning models have been quite successful in terms of real world impact. Large scale applications of machine learning today include Google Search and ads targeting, Siri/Alexa, smart routing on mapping applications, self-piloting drones, defense tech like Anduril, and many other areas. Some areas, like self-driving cars, have shown progress but seem to continuously be "a few years" away every few years. Just as all the ideas for smart phones existed in the 1990s but didn't take place until the iphone launched in 2007, self-driving cars are an inevitable part of the future. In parallel, the machine learning (ML) / artificial intelligence (AI) world has been rocked in the last decade by a series of advancements over time in voice recognition (hence Alexa), image recognition (iphone unlock and the erm, non-creepy, passport controls at Airports). Sequential inventions and discovery include CNNs, RNNs, various forms of Deep Learning, GANs, and other innovations.
On a cloudy Christmas morning last year, a rocket carrying the most powerful space telescope ever built blasted off from a launchpad in French Guiana. After reaching its destination in space about a month later, the James Webb Space Telescope (JWST) began sending back sparkling presents to humanity--jaw-dropping images that are revealing our universe in stunning new ways. Every year since 1988, Popular Science has highlighted the innovations that make living on Earth even a tiny bit better. And this year--our 35th--has been remarkable, thanks to the successful deployment of the JWST, which earned our highest honor as the Innovation of the Year. But it's just one item out of the 100 stellar technological accomplishments our editors have selected to recognize. The list below represents months of research, testing, discussion, and debate. It celebrates exciting inventions that are improving our lives in ways both big and small. These technologies and discoveries are teaching us about the ...
Frontier Airlines has dropped its customer service line in favor of a chat bot, social media, and other text-based methods of communicating. As reported by NPR on Saturday, a company spokesperson said that "the airline found that most customers preferred communicating through online channels." As a consumer, what do you prefer? The use of artificial intelligence (AI) to handle customer service issues is on the rise. What are the benefits and drawbacks?
The variety of tasks that it is solving now and will continue to solve in the next 5 or 10 years will also increase exponentially. As such systems evolve, they naturally become more intuitive, normal and easy to use for the public. Once considered highly mechanical machines with open roof tops and bicycle wheels, resembling more to a horse-cart (than a car as we think in modern terms), which only a particular section of people could afford, not just due to the cost but also the fuel availabilities, now has become highly cheap, comfortable, fuel efficient and safer. Electric cars are even replacing the brilliantly engineered mechanical components into more simpler ones that require minimum maintenance and are much safer for the environment. And let's not get started on autonomous cars.
There was a time when no one could imagine a driverless car would ever exist. But gradually, what we once thought was impossible has become a reality. The first autonomous cars are now commercially available! Although Leonardo da Vinci developed the self-propelled carriage in the 15th century, it was in the 20th century that the concept was realized. When Google announced in 2009 that it would start researching unmanned cars, the idea became even more attractive. Currently, several well-known companies are looking into developing semi-autonomous and fully driverless cars, which could result in significantly fewer traffic accidents.
Explore 5 best artificial intelligence books for beginners to read in 2023. With all the hype around artificial intelligence – robots, autonomous cars, etc -- it can be easy to assume that AI is not having an impact on our daily lives. In fact, most of us encounter artificial intelligence in some form or another almost every day. As soon as you wake up to check your smartphone and watch another movie recommended by Netflix, AI has quickly slipped into our daily lives. Some of its applications include process automation, predictive analysis, fraud detection, customer experience enhancement, and more.
While the popular view is that insights are the key benefit of artificial intelligence, in truth AI creates value by improving the quality of decisions. The good news is, the opportunities for it to do that in business are countless. But because decisions in one area of an organization usually have an impact on decisions in other areas, introducing AI often entails redesigning whole systems. In that way, AI is similar to groundbreaking technologies of the past, like electricity, which initially was used only narrowly but ultimately transformed manufacturing. Decisions involve a combination of prediction and judgment, and because AI makes highly accurate predictions, it will shift decision rights to where judgment is still needed, potentially changing who makes decisions and where, when, and how. More-accurate predictions in one part of a value chain will also have ripple effects on other parts. For instance, if a restaurant can reliably forecast the amount of ingredients it needs each week, its orders will fluctuate, making its suppliers’ sales more uncertain. Strong communication is needed to synchronize effort and resources in a system, and modularity will help prevent changes in one area from disrupting others.
After years of ambitious targets and bold promises, investors are growing impatient with the pace of driverless-car development, applying pressure on an industry that had become accustomed to latitude and piles of cash from investors. Auto makers in recent weeks scaled back plans for the technology amid new pressure to curb expenses during an economic slowdown. An influential hedge fund also has questioned Google-parent Alphabet Inc.'s yearslong effort to advance self-driving technology, an endeavor that has proven thornier than many experts predicted just a few years ago. Activist investor TCI Fund Management this month sent a letter to Alphabet questioning the company's continued spending on its self-driving unit, Waymo. "Waymo has not justified its excessive investments, and its losses should be reduced dramatically," Christopher Hohn, TCI managing director, wrote in the letter.
Idle vehicle relocation is crucial for addressing demand-supply imbalance that frequently arises in the ride-hailing system. Current mainstream methodologies - optimization and reinforcement learning - suffer from obvious computational drawbacks. Optimization models need to be solved in real-time and often trade off model fidelity (hence quality of solutions) for computational efficiency. Reinforcement learning is expensive to train and often struggles to achieve coordination among a large fleet. This paper designs a hybrid approach that leverages the strengths of the two while overcoming their drawbacks. Specifically, it trains an optimization proxy, i.e., a machine-learning model that approximates an optimization model, and then refines the proxy with reinforcement learning. This Reinforcement Learning from Optimization Proxy (RLOP) approach is computationally efficient to train and deploy, and achieves better results than RL or optimization alone. Numerical experiments on the New York City dataset show that the RLOP approach reduces both the relocation costs and computation time significantly compared to the optimization model, while pure reinforcement learning fails to converge due to computational complexity.