The head of the longest-established agency personnel consultancy in the UK says caution should be exercised over the use of Artificial Intelligence in recruitment processes. Property Personnel managing director Anthony Hesse thinks that estate agency should never forget that it is primarily a people business, and technology would have stark weaknesses identifying the right person for the job. "At its heart, estate agency is and has always been about people. We may deal in property, but it is people who are doing the buying and selling. And we are at risk of missing out on some of our best potential recruits if we forget that" explains Hesse. "Artificial Intelligence is all very well; but I seriously doubt that technology will be able to identify some of the subtle factors which only come to light in a one on one relationship between two human beings.

Artificial intelligence (AI) has had a major and positive impact on a range of industries already, with the potential to give much more in the future. We sat down with Ofer Tirosh, CEO of Tomedes, to find out how the translation industry has changed as a result of advances in technology over the past 10 years and what the future might hold in store for it. Translation services have felt the impact of technology in various positive ways during recent years. For individual translators, the range and quality of computer-assisted translation (CAT) tools have increased massively. A CAT tool is a piece of software that supports the translation process.

The drug development lifecycle is long and fraught with heavy risk -- it takes a staggering 10 – 15 years on average, with ultimately only 12 percent of drugs in clinical trials gaining approval by the U.S. Food and Drug Administration (FDA) [1]. To put this in perspective, 22.7 percent of all global research and development spending in 2017 was in the healthcare industry, second only to 23.1 percent spent in the computing and electronics industry, yet the product lifecycle is longer, and costs are much higher [2]. For example, the original iPhone took two and a half years to develop from concept to launch, and an estimated $150 million spent in research and development [3]. In contrast, the average cost of new drug and biologics is $2.87 billion when factoring in the post-approval research and development costs, according to figures released in May 2016 by The Tufts Center for the Study of Drug development (CSDD) [4]. For pharmaceutical companies that have launched more than four drugs, the median cost is closer to a staggering $5.3 billion according to analysis by industry expert Matthew Herper of Forbes [5].

After a prolonged winter, artificial intelligence is experiencing a scorching summer mainly thanks to advances in deep learning and artificial neural networks. To be more precise, the renewed interest in deep learning is largely due to the success of convolutional neural networks (CNNs), a neural network structure that is especially good at dealing with visual data. But what if I told you that CNNs are fundamentally flawed? That was what Geoffrey Hinton, one of the pioneers of deep learning, talked about in his keynote speech at the AAAI conference, one of the main yearly AI conferences. Hinton, who attended the conference with Yann LeCun and Yoshua Bengio, with whom he constitutes the Turin Award–winning "godfathers of deep learning" trio, spoke about the limits of CNNs as well as capsule networks, his masterplan for the next breakthrough in AI.

After a prolonged winter, artificial intelligence is experiencing a scorching summer mainly thanks to advances in deep learning and artificial neural networks. To be more precise, the renewed interest in deep learning is largely due to the success of convolutional neural networks (CNNs), a neural network structure that is especially good at dealing with visual data. But what if I told you that CNNs are fundamentally flawed? That was what Geoffrey Hinton, one of the pioneers of deep learning, talked about in his keynote speech at the AAAI conference, one of the main yearly AI conferences. Hinton, who attended the conference with Yann LeCun and Yoshua Bengio, with whom he constitutes the Turin Award–winning "godfathers of deep learning" trio, spoke about the limits of CNNs as well as capsule networks, his masterplan for the next breakthrough in AI.

The coronavirus pandemic is having a peculiarly American side effect: Gun sales are surging. The stocks of publicly traded guns and ammo companies American Outdoor Brands Corp., Vista Corp., and Sturm Ruger & Co. are up. Sales leaped by more than 19% in January and 17% in February, compared with the same months in 2019, according to Small Arms Analytics & Forecasting. Gun buyers in the United States bought an estimated 1.24 million guns in, January up from 1.04 million the year before, and 1.36 million in February, up from 1.26 million the year before, according to Small Arms Analytics, which bases estimates on background check data. Those millions of new guns are in addition to the approximately 400 million guns American already own.

Perhaps no area of tech has been more buzzy in recent years than the shiny sector of artificial intelligence startups. That happy innovation workshop may have just hit hard times. AI execs and investors say market volatility and regulations clamping down on data may soon lead to AI startups getting gobbled up by companies with cash and data to burn. The market intelligence firm CB Insights said in a report this year that the 100 best-funded startups in artificial intelligence have raised over $7.4B in funding across 300 deals from 600 unique investors. Now as the market skyrockets, then plummets in extreme volatility amid the coronavirus crisis, that may be endangered.

As the continued spread of COVID-19 disrupts workplaces and travel plans around the globe, customer service centers are experiencing an unprecedented uptick in overall call volume. This week, travelers looking to modify their itineraries with Canadian airline WestJet experienced waits in excess of 10 hours. Banks and credit card companies, including Capital One, are seeing longer-than-average hold times, with some customers reporting disconnections. And Google warned Google Store customers to expect "longer than usual" wait times as a result of precautionary health measures that have the company operating with a limited team. As customer representatives are increasingly ordered to work from home in Manila, the U.S., and elsewhere, some companies are turning to AI to bridge the resulting gaps in service.

As the continued spread of COVID-19 disrupts workplaces and travel plans around the globe, customer service centers are experiencing an unprecedented uptick in overall call volume. This week, travelers looking to modify their itineraries with Canadian airline WestJet experienced waits in excess of 10 hours. Banks and credit card companies, including Capital One, are seeing longer-than-average hold times, with some customers reporting disconnections. And Google warned Google Store customers to expect "longer than usual" wait times as a result of precautionary health measures that have the company operating with a limited team. As customer representatives are increasingly ordered to work from home in Manila, the U.S., and elsewhere, some companies are turning to AI to bridge the resulting gaps in service.

In areas such as fraud detection, risk management, credit rating and wealth advisory, AI is already augmenting or even replacing human decision makers. In fact, not deploying AI capabilities in these fields can be considered disastrous. Withthe ever-increasing amounts of data that needs to be processed, AI systems are a must-have to improve accuracy. As technological capabilities continue to improve, the amount of available data grows, and competitive pressures mount, the use of AI in finance will be pervasive. However, as with any new technology the adoption of AI brings its very own set of challenges.