AN ARTIFICIAL intelligence (AI) system has helped British consumer goods giant Unilever save hundreds of thousands pounds a year.

Unilever has claimed it is saving hundreds of thousands of pounds a year by replacing human recruiters with an artificial intelligence system, amid warnings of a populist backlash against the spread of machine learning. The multinational told the Guardian it had saved 100,000 hours of human recruitment time in the last year by deploying software to analyse video interviews. The system scans graduate candidates' facial expressions, body language and word choice and checks them against traits that are considered to be predictive of job success. Vodafone, Singapore Airlines and Intel are among other companies to have used similar systems. Polling commissioned by the Royal Society of Arts and released on Friday suggests 60% of the public are opposed to the use of automated decision-making in recruitment as well as in criminal justice.

A US firm is flogging facial-expression software to analyse job candidates' performance in video interviews and make initial selections for companies including Unilever. The Marmite and Persil maker has previously used facial recognition software to analyse shoppers' reactions to in-store displays of products, and is now turning to algorythms to help in choosing its workforce. Hirevue software claims to analyse facial expressions and language to select the best candidates. Job seekers sit in front of a laptop or mobile phone and complete an automated video interview. This is analysed and compared against results from candidates who have already proved to be good at the job, according to The Telegraph.

India's top multinational conglomerates are in the midst of a digital transformation. Indian companies, not usually viewed as disruptors are now seeing a critical opportunity in leveraging Artificial Intelligence (AI) and Machine Learning (ML) to identify newer opportunities and adapt to the fast-changing business environment. We are seeing a trend where business leaders across industries are deepening their commitment to AI and analytics and seeking ways to apply them at scale. However, making AI work in a conglomerate is not easy. For companies of the scale of Aditya Birla Group, Mahindra & Mahindra and the Tata Group, bigger isn't always better when it comes to driving cross-division synergies and catering to every division's needs.

BAE Systems is a technology partner with robotic process automation (RPA) leader, UiPath, in developing suites of software robots that its customers can use to automate high-volume, repetitive business processes. This press release features multimedia. BAE Systems is now a technology partner with robotic process automation leader, UiPath, to integrate machine learning capabilities into defense and intelligence community programs. "RPAs fuel machine learning tools by feeding them high volumes of structured data necessary for it to begin learning and improving automatically, without being programmed," said Don DeSanto, director of strategic partnerships for the BAE Systems Intelligence & Security sector. "Human-machine teaming is the future of technology, and RPAs serve as workforce multipliers that can be designed to automate many common tasks performed in organizations every day."

Automation, the ability to run processes without human supervision, is one of the most important drivers of increased scalability and productivity. Modern automation largely relies on forms of closed loop control, wherein a controller interacts with a controlled process via actions, based on observations. Despite an increase in the use of machine learning for process control, most deployed controllers still are linear Proportional-Integral-Derivative (PID) controllers. PID controllers perform well on linear and near-linear systems but are not robust enough for more complex processes. As a main contribution of this paper, we examine the utility of extending standard PID controllers with General Dynamic Neural Networks (GDNN); we show that GDNN (neural) PID controllers perform well on a range of control systems and highlight what is needed to make them a stable, scalable, and interpretable option for control. To do so, we provide a comprehensive study using four different benchmark processes. All control environments are evaluated with and without noise as well as with and without disturbances. The neural PID controller performs better than standard PID control in 15 of 16 tasks and better than model-based control in 13 of 16 tasks. As a second contribution of this work, we address the Achilles heel that prevents neural networks from being used in real-world control processes so far: lack of interpretability. We use bounded-input bounded-output stability analysis to evaluate the parameters suggested by the neural network, thus making them understandable for human engineers. This combination of rigorous evaluation paired with better explainability is an important step towards the acceptance of neural-network-based control approaches for real-world systems. It is furthermore an important step towards explainable and safe applied artificial intelligence.

Unilever is using artificial intelligence to influence more of its marketing, from processing insights to finding influencers. The advertiser has 26 data centers across the globe where scientists are using AI to synthesize insights from a range of sources including social listening, CRM and traditional marketing research. Like other advertisers, Unilever hopes the investments fuel a move away from mass reach channels toward more personalized communications that are also cheaper to produce and localize at scale. Unilever has been using AI and machine learning to sort through structured data within a database for years, but it hasn't been able to do the same for unstructured data until recently. Unstructured data is qualitative, which makes gleaning insights from content such as text, audio, social media and mobile activity harder.

Bassam Mohammed Abdelnabi adjusts a combustion test rig in a GE lab in Niskayuna, N.Y., on which researchers have run tests to validate simulations used to develop an AI model that they say will radically cut the time it takes to design products.Courtesy of General Electric Artificial intelligence is helping computers drive cars, recognize faces in a crowd and hold lifelike conversations. General Electric engineers now say they've used the data-intensive technology to develop tools that could cut the industrial giant's design process for jet engines and power turbines in at least half, speeding up its next generation of products. Today, it might take two days for engineers to run a computational analysis of the fluid dynamics of a single design for a turbine blade or an engine component. Scientists at General Electric's research center in Niskayuna, New York, say they've leveraged machine learning to train a surrogate model so that it can evaluate a million variations of a design in just 15 minutes. "This is, we think, a huge breakthrough," Robert Zacharias, technology director of thermosciences at GE Research, tells Forbes.

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