With recent advances, the tech industry is leaving the confines of narrow artificial intelligence (AI) and entering a twilight zone, an ill-defined area between narrow and general AI. To date, all the capabilities attributed to machine learning and AI have been in the category of narrow AI. No matter how sophisticated – from insurance rating to fraud detection to manufacturing quality control and aerial dogfights or even aiding with nuclear fission research – each algorithm has only been able to meet a single purpose. This means a couple of things: 1) an algorithm designed to do one thing (say, identify objects) cannot be used for anything else (play a video game, for example), and 2) anything one algorithm "learns" cannot be effectively transferred to another algorithm designed to fulfill a different specific purpose. For example, AlphaGO, the algorithm that outperformed the human world champion at the game of Go, cannot play other games, despite those games being much simpler.

Rapid prototyping, the fast and cost-effective production of prototypes or models -- better known as 3D printing -- has long since established itself as an important tool for industry. "If this concept could be transferred to the nanoscale to allow individual molecules to be specifically put together or separated again just like LEGO bricks, the possibilities would be almost endless, given that there are around 1060 conceivable types of molecule," explains Dr. Christian Wagner, head of the ERC working group on molecular manipulation at Forschungszentrum Jülich. There is one problem, however. Although the scanning tunnelling microscope is a useful tool for shifting individual molecules back and forth, a special custom "recipe" is always required in order to guide the tip of the microscope to arrange molecules spatially in a targeted manner. This recipe can neither be calculated, nor deduced by intuition -- the mechanics on the nanoscale are simply too variable and complex.

The design of real-world automated control systems that do everything from regulating the temperature of skyscrapers to running the widget-making machine in the widget factory down the street requires expertise in sophisticated physics-based modeling. The need for this modeling expertise increases operational costs and restricts the applicability of automated control to systems in which marginal operational performance improvements lead to huge economic benefits, according to data scientists. With unlimited access to supercomputers and mountains of data, engineers can train artificial intelligence systems such as deep neural networks, a type of machine learning model, to perform automated control. But many people lack access to the necessary computational power to do so, or the ability to generate the amount of data needed to train a controller that has a deep neural network. What's more, these types of deep neural networks are so-called black-box models, which means that the factors they use to make decisions are hidden from the end user.

The next layer embodies the semi autonomous weapons which despite lifting some of the risk off the human operator, they can't assume full responsibility since the human operator still has the final say. Examples of those kind of weapons are the fire-and-forget air-to-air missiles or aircraft which automatically lock onto their targets. Pressing the button to launch such missiles is still the pilot's responsibility... Clearly the danger lurks in the third level, that of the fully autonomous robots where no human intervention is required. These robots are able to act on their own and accomplish their mission without the emotional burden or ethical reservations there to stop them.

Bezos said in the 2013 interview that it would take four or five years to have those drone deliveries. It turns out that using remote-controlled aerial gizmos to drop stuff at our homes is incredibly difficult, prone to risk and potentially more trouble than it's worth. Like driverless cars, drone technology in populated areas is more complicated than most people expected, and it has been -- mostly for good reason -- tightly controlled in the United States by government agencies worried about drones straying into the path of airplanes, dropping out of the sky onto our heads or unwittingly spying through people's windows. It wasn't until this week that the F.A.A. gave Amazon permission to do drone deliveries. And drones might never be practical for deliveries when someone in a vehicle could do the same thing in a fraction of the time and cost. Drones are a great public relations jolt for Amazon, but let's not put too much stock in them for awhile -- maybe ever.

Recently, researchers from DeepMind and McGill University proposed new approaches to speed up the solution of complex reinforcement learning problems. They mainly introduced a divide and conquer approach to reinforcement learning (RL), which is combined with deep learning to scale up the potentials of the agents. For a few years now, reinforcement learning has been providing a conceptual framework in order to address several fundamental problems. This algorithm has been utilised in several applications, such as to model robots, simulate artificial limbs, developing self-driving cars, play games like poker, Go, and more. Also, the recent combination of reinforcement learning with deep learning added several impressive achievements and is found to be a promising approach to tackle important sequential decision-making problems that are currently intractable.

Danish physicist Neils Bohr once quipped that prediction is hard, especially when it is about the future. But this is precisely what financial regulators need to do--forecasting the likely state of the economy in the future is crucial when deciding on policy levers like whether to slash or raise interest rates. However, as the world continues to become more unpredictable, forecasting has become increasingly difficult. This challenge was poignantly illustrated after the start of the 2008 Financial Crisis, when Queen Elizabeth asked a seemingly simple but pointed question to a room of researchers and economists at the London School of Economics: Why did no one see it coming? In the face of great complexity, perhaps econometrics could do with more help.

New automated harvesters like the Harvest CROO Robotics strawberry robot utilizes AI to capture images of ripe berries ready to pick. Artificial intelligence, or AI as it is more commonly called, has become more prominent in conversations about technology these days. But what does it mean? And how might it shape the future of agriculture? In many ways, AI is already at work in agricultural research and in-field applications, but there is much more to come.

David Carbon, the vice president of Prime Air, said in a statement that the certification "indicates the F.A.A.'s confidence in Amazon's operating and safety procedures for an autonomous drone delivery service that will one day deliver around the world." He added that the company would "continue to develop and refine our technology to fully integrate delivery drones into the airspace, and work closely with the F.A.A. and other regulators around the world to realize our vision of 30-minute delivery." At a conference in Las Vegas last year, Amazon revealed a fully electric hexagonal drone that could carry up to five pounds. The drone had advanced spatial awareness technology that allowed it to avoid contact with other objects, the company said. Amazon already offers one-day delivery in many places, but shortening delivery times has long been a goal of the company's chief executive, Jeff Bezos.

In July 2019, Tesla's website advertised "full potential for autonomous driving" including automatic driving on motorways - although it has always maintained that its Autopilot is not designed to be a substitute for a human driver.