Paris, March 26, 2019 – A new study from the Capgemini Research Institute has found that just 10% of major automotive companies are implementing artificial intelligence[1] (AI) projects at scale, with many falling short of an opportunity that could increase operating profit by up to 16%. The research also shows that fewer automotive companies are implementing AI than was the case in 2017, despite the cost, quality and productivity advantages, many report it delivering. The "Accelerating Automotive's AI Transformation: How driving AI enterprise-wide can turbo-charge organizational value" study surveyed 500 executives from large automotive companies in eight countries, building on comparable study from 2017, to establish recent trends in AI investment and deployment. Scaling of AI has seen a slow growth: Since 2017, the number of automotive companies that have successfully scaled AI implementation has increased only marginally (from 7% to 10%). However, more significant was the increase in companies not using AI at all (from 26% to 39%).

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The United States Postal Service is going to put mail on self-driving trucks. Starting this week, letters and packages moving between Phoenix and Dallas will travel on customized Peterbilt trucks run by TuSimple, an autonomous startup based in San Diego. There will be five round trips between the two cites, with the first haul leaving from Phoenix this morning. It's the first time that the Postal Service has contracted with an autonomous provider for long-haul service. "This pilot is just one of many ways the Postal Service is innovating and investing in its future," the USPS said in a press release that cited the possibility of using "a future class of vehicles" to improve service, reduce emissions and save money. After the initial trial, which is expected to last about two weeks, the Postal Service will assess whether to continue working with TuSimple.

When Conor Sprouls, a customer service representative in the call center of insurance giant MetLife talks to a customer over the phone, he keeps one eye on the bottom-right corner of his screen. There, in a little blue box, A.I. tells him how he's doing. The program flashes an icon of a speedometer, indicating that he should slow down. A heart icon pops up. For decades, people have fearfully imagined armies of hyper-efficient robots invading offices and factories, gobbling up jobs once done by humans.

The economy ministry plans to start testing unmanned ground vehicles on public roads by the end of March next year through cooperation with private-sector companies, hoping to put them into practical use soon. The ministry agreed Monday to establish a public-private council on UGVs. Members include e-commerce firm Rakuten Inc., Yamato Transport Co., Japan Post Co. and transport company Seino Holdings Co., as well as the National Police Agency, the transport ministry and local governments. The council is to identify challenges, including ways to secure the safety of UGVs on public roads and who would bear responsibility for accidents, officials said. The ministry is considering possible revisions to the road traffic law in fiscal 2020, which starts in April next year.

Since their introduction more than a decade ago, smartphones have been equipped with cameras, allowing users to capture images and video without carrying a separate device. Thanks to the use of computational photographic technologies, which utilize algorithms to adjust photographic parameters in order to optimize them for specific situations, users with little or no photographic training can often achieve excellent results. The boundaries of what constitutes computational photography are not clearly defined, though there is some agreement that the term refers to the use of hardware such as lenses and image sensors to capture image data, and then applying software algorithms to automatically adjust the image parameters to yield an image. Examples of computational photography technology can be found in most recent smartphones and some standalone cameras, including high dynamic range imaging (HDR), auto-focus (AF), image stabilization, shot bracketing, and the ability to deploy various filters, among many other features. These features allow amateur photographers to produce pictures that can, at times, rival photographs taken by professionals using significantly more expensive equipment.

A host of different tasks--such as identifying the song in a database most similar to your favorite song, or the drug most likely to interact with a given molecule--have the same basic problem at their core: finding the point in a dataset that is closest to a given point. This "nearest neighbor" problem shows up all over the place in machine learning, pattern recognition, and data analysis, as well as many other fields. Yet the nearest neighbor problem is not really a single problem. Instead, it has as many different manifestations as there are different notions of what it means for data points to be similar. In recent decades, computer scientists have devised efficient nearest neighbor algorithms for a handful of different definitions of similarity: the ordinary Euclidean distance between points, and a few other distance measures.

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First, before I start, I want to say something about what that is, or what I understand from this. So, here is one interpretation. It is about using data, obviously. So, it has relationships to analytics and data science, and it is, obviously, part of AI in some way. This is my little taxonomy, how I see things linking together. You have computer science, and that has subfields like AI, software engineering, and machine learning is typically considered to be subfield of AI, but a lot of principles of software engineering apply in this area. This is what I want to talk about today. It's heavily used in data science. So, the difference between AI and data science is somewhat fluid if you like, but data science tries to understand what's in data and tries to understand questions about data. But then it tries to use this to make decisions, and then we are back at AI, artificial intelligence, where it's mostly about automating decision making. We have a couple of definitions. AI means using intelligence, making machines intelligent, and that means you can somehow function appropriate in an environment with foresight. Machine learning is a field that looks for algorithms that can automatically improve their performance without explicit programming, but by observing relevant data. And yes, I've thrown in data science as well for good measure, the scientific process of turning data into insight for making better decisions. If you have opened any newspaper, you must have seen the discussion around the ethical dimensions of artificial intelligence, machine learning or data science. Testing touches on that as well because there are quite a few problems in that space, and I'm just listing two here. So, you use data, obviously, to do machine learning. Where does this data come from, and are you allowed to use it? Do you violate any privacy laws, or are you building models that you use to make decisions about people? If you do that, then the general data protection regulation in the EU says you have to be able to explain to an individual if you're making a decision based on an algorithm or a machine, if this decision is of any kind of significant impact. That means, in machine learning, a lot of models are already out of the door because you can't do that. You can't explain why a certain decision comes out of a machine learning model if you use particular models.

A pioneering new clinical trial on a drug that could potentially help millions of men with prostate cancer, is under way in Derry. Tumour samples from men being treated locally are being collated to test the drug's effectiveness at the Clinical Transitional Research and Innovation Centre (C-TRIC), labs on the Altnagelvin Hospital site. The new trials are the result of a partnership with American pharmaceutical company Lantern Pharma and the PRAISE (prostate cancer artificial intelligence study using ex vivo models) trial is using artificial intelligence to test a cancer drug called LP-184 to predict which types of tumours are sensitive to it. The company said the groundbreaking work, which is partially funded by Invest NI, does not involve human or animals trials due to the use of AI. The new project will help guide future cancer research and clinical trials and early indications suggest there could also be benefits for research into the treatment of ovarian and liver cancer.