The invention of artificial things that learn and perform actions took place in the classic times. Alongside Calculus Ratiocinator by Llull, there were many fictional stories and dramas depicting artificial things and their immense potentials. You must watch it if you haven't. Church-Turing thesis -- which means machines can simulate any process of formal reasoning (from Wiki). Theory that backed up the brains of creators like Allen Newell, Herbert Simon, John McCarthy, Marvin Minsky, and Arthur Samuel.

Belief Propagation algorithms are instruments used broadly to solve graphical model optimization and statistical inference problems. In the general case of a loopy Graphical Model, Belief Propagation is a heuristic which is quite successful in practice, even though its empirical success, typically, lacks theoretical guarantees. This paper extends the short list of special cases where correctness and/or convergence of a Belief Propagation algorithm is proven. We generalize formulation of Min-Sum Network Flow problem by relaxing the flow conservation (balance) constraints and then proving that the Belief Propagation algorithm converges to the exact result.

Introducing inequality constraints in Gaussian process (GP) models can lead to more realistic uncertainties in learning a great variety of real-world problems. We consider the finite-dimensional Gaussian approach from Maatouk and Bay (2017) which can satisfy inequality conditions everywhere (either boundedness, monotonicity or convexity). Our contributions are threefold. First, we extend their approach in order to deal with general sets of linear inequalities. Second, we explore several Markov Chain Monte Carlo (MCMC) techniques to approximate the posterior distribution. Third, we investigate theoretical and numerical properties of the constrained likelihood for covariance parameter estimation. According to experiments on both artificial and real data, our full framework together with a Hamiltonian Monte Carlo-based sampler provides efficient results on both data fitting and uncertainty quantification.

Roberta doesn't have a last name, a face, or arms. She is the first piece of robotic software to work in the Norwegian company's treasury department, part of Statoil's push toward automation, robotics and artificial intelligence, said Mr. Kjøllesdal, acting head of internal treasury.

Electric vehicles are the main market for Romeo Power Technology, the Vernon-based lithium-ion battery pack startup. But the company is also using its know-how to make portable battery packs for individuals. On Thursday it introduced the Saber, a ruggedized 2.2-pound bar-shaped device the company says can quick-charge a smartphone 10 times -- or, with the right connectors, 10 smartphones at once. It can also recharge tablets, laptops and small drone aircraft as fast as a wall charger, the company said -- it's "like having a wall-socket in your pocket," said Dion Isselhardt, the company's chief product officer. The Saber itself requires two hours of wall time for a refill.

Many years ago, during my first assignment at (super) major oil company, I was in charge of significant decisions for wells drilled in an onshore gas field. Each of these wells were drilled quickly, on average taking 5–7 days. The geology was well known, the reservoirs were, generally speaking, economic and the operational risks from drilling were rather low and manageable.

Just as oil propelled Standard Oil Co. Inc. to a position of dominant industrial power in the late 1800s, data is doing the same for a number of technology firms today. Half of consumer online spending in the U.S. is controlled by Amazon, a company that relies extensively on mining data so it knows what you want before your buy it.

In today's competitive business environment, power plant owners are constantly striving to reduce their operation and maintenance costs, thus increasing their profits. To enable plant owners to operate their plants more efficiently, it is important to develop advanced digital solutions (software and tools) that can provide decision support for the plant operation optimization. For example, Digital Power Plant, a part of the GE's vision for the digitization of industrial assets, is one of such technologies recently developed in GE. Digital Power Plant involves building a collection of digital models (both physics-based and datadrive), or "Digital Twins" as we call it at GE, which are used to model the present state of every asset in a power plant. This transformational technology enables utilities to monitor and manage every aspect of the power generation ecosystem to generate electricity as cleanly, efficiently, and securely.

While working at Tesla, I always enjoyed talking to people after they finished a factory tour. As much as they raved about the amazing automation, gigantic presses, and hundreds of robots, the reality was they only saw half of the actual manufacturing that was taking place in the building. Unknown to most visitors, the factory's "secret" second floor built many of Tesla's battery, power electronics, and drive-train systems. It was home to some of the most advanced manufacturing and automation systems in the company. Some of the robots moved at such high speeds that their arms needed to be built from carbon fiber instead of steel.

In the future, industrial automation will continue to decrease the need for human workers in the developed world. Jobs like trucking are slowly being transferred to self-driving trucks, as businesses can save a good deal of money on labor over the long term. Even white-collar careers such as the paralegal and clerical fields are highly susceptible to automation by computer programs. So how can one ensure that they will be employable as this drastic societal change occurs? This problem can be mitigated by observing trends in the economy and finding sectors that will grow long into the future.