The innovative power of the German capital in the area of AI is not only noticeable in the high-profile areas of business intelligence and process management, but is also demonstrated by the excellent work of the AI companies which deal intensively with intelligent health and represent about 10 per cent of the Berlin AI ecosystem. AI systems from Berlin are used in a variety of ways: they help in the diagnosis and data analysis of specific disease patterns, but are also used in operation planning and in supporting the internal processes of hospitals. Apps for intelligent data recording and analysis in the field of prevention are being developed in the context of fitness and health. Chatbots, i.e. systems with which people can communicate in natural language, also accompany patients during the healing process. A number of start-ups in Berlin are pushing the boundaries of traditional healthcare with innovative solutions which could also break new ground on the international stage - always at the interface between business and research.

To most of us, a 3-D-printed turtle just looks like a turtle; four legs, patterned skin, and a shell. But if you show it to a particular computer in a certain way, that object's not a turtle -- it's a gun. Objects or images that can fool artificial intelligence like this are called adversarial examples. Jessy Lin, a senior double-majoring in computer science and electrical engineering and in philosophy, believes that they're a serious problem, with the potential to trip up AI systems involved in driverless cars, facial recognition, or other applications. She and several other MIT students have formed a research group called LabSix, which creates examples of these AI adversaries in real-world settings -- such as the turtle identified as a rifle -- to show that they are legitimate concerns.

Voice and the rise of home devices and smart speakers are opening up new possibilities for researchers, enabling respondents to engage beyond simply typing a response and creating opportunities for ongoing dialogue. In an ESOMAR paper, What market research can learn from Alexa & Siri, a trio of authors – Young Ham (Kantar Australia), Jason Dodge (Kantar US) and Rebecca Southern (Kantar Australia) – extol the benefits of chatbots and AI. "These can help bridge the gap between quantitative and qualitative, offering more in-depth ways to better understand today's consumers," they write. "These give the chance to participate in a more interactive, flowing manner that is more conversational than a typed response." And for marketing and insights teams, they add, "AI can deliver smarter, more impactful consumer engagement... at scale".

Artificial intelligence has set its sights on the recruitment industry. More and more companies are beginning to use AI-solutions to help speed up the process and make it less hit or miss. Here we highlight 7 areas of recruitment where AI might be used to lighten the load for recruiters and find better candidates for their organization and positions. Artificial intelligence is finding many applications in various industries, and recruitment is no different. AI can be employed to help the recruitment process for companies by employing machine-learning and problem-solving to help find the best candidate(s) for a position. "This new technology is designed to streamline or automate some part of the recruiting workflow, especially repetitive, high-volume tasks.

Judges are far from being infallible. For example, in psychologist Daniel Kahneman's book Thinking Fast and Slow, it was shown that there is a correlation between the leniency of a judge in court, and how recently they had eaten lunch. Is there a way to get around this problem? According to China and Estonia, AI should be the judge - literally. China has a history in unveiling surprising AI counterparts for professionals whose jobs most would expect to be relatively safe from AI.

Companies that use artificial intelligence are strengthening their standards for the technology, creating governance policies and hiring executives to make sure their algorithms meet ethical and regulatory requirements. Massachusetts Mutual Life Insurance Co. and analytics software company Fair Isaac Co., creator of the widely used FICO credit score, are both rolling out in-house technology systems designed in part to track whether AI algorithms adhere to a set of standards. FICO is using blockchain for the project.

Customer experience professionals have been obsessed with mapping customer journeys -- optimising business processes and streamlining the passage of engagement. These maps or flowcharts are meticulously designed to guide customers effortlessly from Point A to Point B, to complete their purchase, or to get help. Yet prospects and customers still get off the planned route, choose a different path, or simply get distracted. In business, it's imperative to guide prospects and customers to where they should be. We need to shape customer journeys -- in real time -- not just map them and hope they follow the directions.

The global drug discovery market is estimated to be worth at least $35 billion, a figure that could rise to $71 billion by 2025. But taking a drug from research and development to market is a long and resource-intensive process. A large part of this work involves rigorous testing to ensure a drug is not only effective, but safe -- and this unfortunately entails animal testing, whether on monkeys, rats, mice, dogs, or rabbits. Contrary to what some may think, animal testing is not only a pivotal facet of drug development, in most countries it's actually a legal requirement that must be completed before clinical trials on humans can commence. However, animal testing is slow and expensive, with a low success rate -- it's estimated that fewer than 10% of drug candidates tested on animals make it through the pipeline.

JERUSALEM (Reuters) - Intel Corp on Tuesday launched its latest processor that will be its first using artificial intelligence (AI) and is designed for large computing centers. The chip, developed at its development facility in Haifa, Israel, is known as Nervana NNP-T or Springhill and is based on a 10 nanometer Ice Lake processor that will allow it to cope with high workloads using minimal amounts of energy, Intel said. Intel said its first AI product comes after it had invested more than $120 million in three AI startups in Israel. "In order to reach a future situation of'AI everywhere', we have to deal with huge amounts of data generated and make sure organizations are equipped with what they need to make effective use of the data and process them where they are collected," said Naveen Rao, general manager of Intel's artificial intelligence products group. "These computers need acceleration for complex AI applications."

Few ideas in the last decade have provoked as much excitement, or as much confusion, as the introduction of artificial intelligence (AI) in oncology. From the first moment we announced our plans to apply our Watson technology to help oncologists, we were met with a stark dichotomy of emotion. The headlines ran the spectrum from hype (your next doctor might be a robot!) to cynicism (5 reasons AI in healthcare will fail). Today, five years into the journey to help improve cancer treatment through data, analytics and AI, while we're still very much in the early stages, I'm happy to report that the real-world progress is far more encouraging than either of those early storylines would suggest. In fact, not only is AI being used to support physicians in the delivery of cancer care today, it is producing quantifiable results while charting a course for the future.