Agriculture is set to become'smarter' with digitisation and new technologies such as facial recognition for animals promising to provide the sector with greater control over its processes. Dr. Venkat Maroju, Chief Executive Officer of'SourceTrace' a provider of software solutions to the agriculture and allied sectors, said that digital technologies have an "enormous potential" to impact agriculture in several aspects by enabling farmers, on the one hand, to produce more while at the same time reducing the environmental impact of agricultural production. "Digital technology comes with several solutions to make this happen from farm management and traceability to certification and market linkage," he said. In September, itelligence AG the SAP software and technologies services company in partnership with the German Technical University OWL and HARTING Foundation & Co. KG a provider of industrial interconnection technology announced the launch of HARTING MICA . This is a system designed to enable the most efficient method of farming a given acreage of wheat using data collected via sensors from the soil, agricultural machinery and satellite images.

Gartner, Inc. today revealed its top strategic predictions for 2020 and beyond. Gartner's top predictions examine how the human condition is being challenged as technology creates varied and ever-changing expectations of humans. "Technology is changing the notion of what it means to be human," said Daryl Plummer, distinguished vice president and Gartner Fellow. "As workers and citizens see technology as an enhancement of their abilities, the human condition changes as well. CIOs in end-user organizations must understand the effects of the change and reset expectations for what technology means."

A recent study conducted by Oracle and research firm Future Workplace found that 64% of people would trust a robot more than their manager. The study included 8,370 employees, managers and HR leaders across 10 countries. Its aim was to see how AI has changed relationships between people and technology at work. It did have some surprising results when comparing human supervisors to potential robot overlords. According to the study, 64 % of people would trust a robot over their manager.

Google has updated its search algorithms to tap into an AI language model that is better at understanding netizens' queries than previous systems. Pandu Nayak, a Google fellow and vice president of search, announced this month that the Chocolate Factory has rolled out BERT, short for Bidirectional Encoder Representations from Transformers, for its most fundamental product: Google Search. To pull all of this off, researchers at Google AI built a neural network known as a transformer. The architecture is suited to deal with sequences in data, making them ideal for dealing with language. To understand a sentence, you must look at all the words in it in a specific order.

In a brightly coloured shipping container in east London, Rubens Filho is asking me to pick a card. "Any card," he says, fanning the pack out face down. "And don't worry, you can show me. I pull out the seven of spades, and show it to him; he gets me to sign my name on it with a marker pen. Then he slides it back into the middle of the pack, puts the cards back into their box and puts the box on the table in front of us. "Now," he says with a grin, "the magic begins." Filho is 51, tall, handsome and infectiously enthusiastic about the power of magic tricks and illusions. Born in Brazil, he's been a keen magician since adolescence. He came to Britain in 2012 to work in advertising, before, in 2015, setting up Abracademy, a startup dedicated to bringing magic – and in particular the skills needed to perform it – to the rest of us. "I think magic has a such a positive twist," he says. "It brings this soft approach that's hard to explain, this role of creating something beautiful." But he is also fascinated by the relationship between magic and neuroscience and psychology, and set up Abracademy Labs, an offshoot of Abracademy, to explore this connection. "Magic has lived in the'glitches' of the brain for a long time," he says. "How you see things, how you form beliefs, how you experience wonder.

The reports provide market insights into demand drivers, regional outlook, and competitive analysis of the Cognitive Computing market for the Cognitive Computing forecast period. Further, it throws focus on restraints as well discusses future chances at length that are likely to come to the fore over the forecast period. The analysis thus provided helps market stakeholders with business planning and to gauge scope of expansion in the Cognitive Computing market over the forecast period. Moreover, the report has explored changing factors for the market segments. It covers the growth factors of the worldwide market based on end-users. It's a well-crafted Cognitive Computing market research report which has been designed using the primary and secondary sources.

In part 1 of this article we discussed the industry trend of companies wanting to brand themselves as "AI first" and often positioning themselves as deep learning. We highlighted some of the problems building and deploying a deep learning solution presents and suggest that often other machine learning approaches could provide a solution in a simpler and more cost effective way. In this second part we want to outline our own experience building an AI application and reflect on why we chose not to utilise deep learning as the core technology used. At Aiqudo we have built a personal digital assistant for smart phones. Our goal is to understand what users are saying, figure out their intent and execute the correct action for them on their devices..

This paper deals with the design of a sensing matrix along with a sparse recovery algorithm by utilizing the probability-based prior information for compressed sensing system. With the knowledge of the probability for each atom of the dictionary being used, a diagonal weighted matrix is obtained and then the sensing matrix is designed by minimizing a weighted function such that the Gram of the equivalent dictionary is as close to the Gram of dictionary as possible. An analytical solution for the corresponding sensing matrix is derived which leads to low computational complexity. We also exploit this prior information through the sparse recovery stage and propose a probability-driven orthogonal matching pursuit algorithm that improves the accuracy of the recovery. Simulations for synthetic data and application scenarios of surveillance video are carried out to compare the performance of the proposed methods with some existing algorithms. The results reveal that the proposed CS system outperforms existing CS systems.

Google on Friday announced its "biggest leap forward" in years in its search algorithm, offering an unusually detailed public explanation of its secret formula. The world's most popular internet search engine said its latest refinement uses machine learning to improve how it handles conversationally phrased English-language requests. "We're making a significant improvement to how we understand queries, representing the biggest leap forward in the past five years, and one of the biggest leaps forward in the history of search," Google search vice president Pandu Nayak said in an online post. The California-based internet company last year debuted a neural network-based technique for processing "natural language." The company said the new effort is based on what it calls Bidirectional Encoder Representations from Transformers (BERT), which seeks to understand query words in the context of sentences for insights, according to Nayak.

SAN FRANCISCO – Google on Friday announced its "biggest leap forward" in years in its search algorithm, offering an unusually detailed public explanation of its secret formula. The world's most popular internet search engine said its latest refinement uses machine learning to improve how it handles conversationally phrased English-language requests. "We're making a significant improvement to how we understand queries, representing the biggest leap forward in the past five years, and one of the biggest leaps forward in the history of search," Google search vice president Pandu Nayak said in an online post. The California-based internet company last year debuted a neural network-based technique for processing "natural language." The company said the new effort is based on what it calls Bidirectional Encoder Representations from Transformers (BERT), which seeks to understand query words in the context of sentences for insights, according to Nayak.