WhatsApp's hotly anticipated'Recall' feature has moved a step closer to reality. The new functionality, which would allow you to delete things you've already sent, is expected to be enabled by the company imminently. Recall – also widely known as Unsend and Revoke – will work on all types of messages, including texts, images, videos, GIFs, documents, quoted messages and even Status replies, but only if they were sent within a five-minute window. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

As you contemplate the introduction of artificial intelligence, you should articulate what mix of three approaches works best for you. Jeff Heepke knows where to plant corn on his 4,500-acre farm in Illinois because of artificial intelligence (AI). He uses a smartphone app called Climate Basic, which divides Heepke's farmland (and, in fact, the entire continental U.S.) into plots that are 10 meters square. The app draws on local temperature and erosion records, expected precipitation, soil quality and other agricultural data to determine how to maximize yields for each plot. If a rainy cold front is expected to pass by, Heepke knows which areas to avoid watering or irrigating that afternoon. As the U.S. Department of Agriculture noted, this use of artificial intelligence across the industry has produced the largest crops in the country's history. Climate Corp., the Silicon Valley–based developer of Climate Basic, also offers a more advanced AI app that operates autonomously. If a storm hits a region, or a drought occurs, it lowers local yield numbers.

Motherboard has called Ray Kurzweil "a prophet of both techno-doom and techno-salvation." With a little wiggle room given to the timelines the author, inventor, computer scientist, futurist, and director of engineering at Google provides, a full 86 percent of his predictions -- including the fall of the Soviet Union, the growth of the internet, and the ability of computers to beat humans at chess -- have come to fruition. Kurzweil continues to share his visions for the future, and his latest prediction was made at the most recent SXSW Conference, where he claimed that the Singularity -- the moment when technology becomes smarter than humans -- will happen by 2045. Sixteen years prior to that, it will be just as smart as us. As he told Futurism, "2029 is the consistent date I have predicted for when an AI will pass a valid Turing test and therefore achieve human levels of intelligence."

Artificial Intelligence (AI) is one of the most exciting and transformative opportunities of our time. From my vantage point as a venture investor at Playfair Capital, where I focus on investing and building community around AI, this is a great time for investors to help build companies in this space. There are three key reasons. First, with 40 percent of the world's population now online, and more than 2 billion smartphones being used with increasing addiction every day (KPCB), we're creating data assets, the raw material for AI, that describe our behaviors, interests, knowledge, connections and activities at a level of granularity that has never existed.

In 2011, Apple became the first company to place artificial intelligence in the pockets of millions of consumers when the firm's co-founder Steve Jobs launched the voice assistant Siri on the iPhone. Eight years later, the technology giant is struggling to find its voice in AI. Analysts say the question of whether Apple can succeed in building great artificial intelligence products is as fundamental to the company's next decade as the iPhone was to its previous one. But the tech giant faces a formidable dilemma because the nature of artificial intelligence pushes Apple far out of its comfort zone in sleekly-designed hardware and services. AI programming demands a level of data collection and mining that is at odds with Apple's rigorous approach to privacy, as well as its positioning as a company that doesn't profile consumers.

In San Francisco, a young engineer hopes to "optimise" his life through sensors that track his heart rate, respiration and sleep cycle. In Copenhagen, a bus running two minutes behind schedule transmits its location and passenger count to the municipal traffic signal network, which extends the time of the green light at each of the next three intersections long enough for its driver to make up some time. In Davao City in the Philippines, an unsecured webcam overlooks the storeroom of a fast food stand, allowing anyone to peer in on all its comings and goings. What links these wildly different circumstances is a vision of connected devices now being sold to us as the "internet of things". The technologist Mike Kuniavsky, a pioneer of this idea, characterises it as a state of being in which "computation and data communication [are] embedded in, and distributed through, our entire environment".

Inside the sprawling Acer stall at Computex Taipei, Asia's largest tech show, staff displayed a laptop computer that's ready for virtual reality play yet thinner than most PCs for gaming. At the same exhibition, the Taiwanese tech hardware maker showed how its internet cloud uses artificial intelligence to predict what customers will do when shopping and allow the shop to make decisions accordingly. Acer was riding two major new themes at the annual show: virtual reality, often abbreviated to VR, and artificial intelligence, or AI. Demand from gamers, a lucrative market of people willing to pay more than $10,000 for a personal computer (PC), is driving the VR side, compelling Acer and its peers to install new lines of processors that support immersive, 3D play with headgear and hand controls. "You can see that the company is moving into more gaming centric, VR, new experience innovation," said Vincent Lin, senior director of Acer's global product marketing.

In the United States, job demographic data from censuses since the 1900s reveal a startling fact. Despite the two post-Industrial revolutions of electricity and computers, the occupations with the largest employment numbers are still jobs for drivers, retail, cashiers, secretaries, janitors etc, i.e. old professions needing simple skills and mostly repetitive work. This lack of transition to "newer" jobs is a global phenomenon, especially in the global south. India, for example, has half of the working population doing agriculture. One must grasp the significance of Artificial Intelligence (AI) in this context.

It is challenging to develop stochastic gradient based scalable inference for deep discrete latent variable models (LVMs), due to the difficulties in not only computing the gradients, but also adapting the step sizes to different latent factors and hidden layers. For the Poisson gamma belief network (PGBN), a recently proposed deep discrete LVM, we derive an alternative representation that is referred to as deep latent Dirichlet allocation (DLDA). Exploiting data augmentation and marginalization techniques, we derive a block-diagonal Fisher information matrix and its inverse for the simplex-constrained global model parameters of DLDA. Exploiting that Fisher information matrix with stochastic gradient MCMC, we present topic-layer-adaptive stochastic gradient Riemannian (TLASGR) MCMC that jointly learns simplex-constrained global parameters across all layers and topics, with topic and layer specific learning rates. State-of-the-art results are demonstrated on big data sets.

Multi-Task Learning (MTL) can enhance a classifier's generalization performance by learning multiple related tasks simultaneously. Conventional MTL works under the offline or batch setting, and suffers from expensive training cost and poor scalability. To address such inefficiency issues, online learning techniques have been applied to solve MTL problems. However, most existing algorithms of online MTL constrain task relatedness into a presumed structure via a single weight matrix, which is a strict restriction that does not always hold in practice. In this paper, we propose a robust online MTL framework that overcomes this restriction by decomposing the weight matrix into two components: the first one captures the low-rank common structure among tasks via a nuclear norm and the second one identifies the personalized patterns of outlier tasks via a group lasso. Theoretical analysis shows the proposed algorithm can achieve a sub-linear regret with respect to the best linear model in hindsight. Even though the above framework achieves good performance, the nuclear norm that simply adds all nonzero singular values together may not be a good low-rank approximation. To improve the results, we use a log-determinant function as a non-convex rank approximation. The gradient scheme is applied to optimize log-determinant function and can obtain a closed-form solution for this refined problem. Experimental results on a number of real-world applications verify the efficacy of our method.