The semiconductor industry is looking towards recovery strategies. Software has been "the star of high-tech" over the years, but hardware is the core enabler of innovation. As businesses and consumers alike latch on to the advantages of AI applications, whether it's virtual assistants or facial recognition systems, there is a resurging need for advanced hardware. Deloitte describes semiconductors as "essential technology enablers" that power many of the cutting-edge digital devices we use today. By providing next generation accelerator architectures, semiconductor companies can increase computational efficiency or facilitate the transfer of large data sets through memory or storage, crucial for machine learning and AI development.
Yesterday, AIM published an article on how difficult it is for the small labs and individual researchers to persevere in the high compute, high-cost industry of deep learning. Today, the policymakers of the US have introduced a new bill that will ensure deep learning is affordable for all. The National AI Research Resource Task Force Act was introduced in the House by Representatives Anna G. Eshoo (D-CA) and her colleagues. This bill was met with unanimous support from the top universities and companies, which are engaged in artificial intelligence (AI) research. Some of the well-known supporters include Stanford University, Princeton University, UCLA, Carnegie Mellon University, Johns Hopkins University, OpenAI, Mozilla, Google, Amazon Web Services, Microsoft, IBM and NVIDIA amongst others.
Several cities have banned police from using facial recognition software, which has well-known racial bias issues (and many false-positive issues as well). Detroit, however, has a very public debate in 2019 about the use of facial recognition, and instead decided to regulate its use rather than ban it altogether. Late last year, the city adopted a policy, which bans the use of facial recognition to "surveil the public through any camera or video device," bans its use on livestream and recorded videos, and restricts (but does not ban) its use at protests. According to the policy, the software must be used only "on a still image of an individual," and can only be used as part of an ongoing criminal investigation. The software checks images across a state database of photos, which include mugshot images.
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Fighter jets belonging to a Saudi-led coalition battling Yemen's Houthi rebels have launched dozens of air raids on several Yemeni provinces, as the kingdom announced the start of a new military operation. The Houthi-run Al Masirah Media Network reported air raids on the capital, Sanaa, as well as Marib, al-Jouf, al-Bayda, Hajjah and Saada provinces throughout Wednesday and into the night. It said an elderly woman and a child were killed and four others wounded in Saada province. In Sanaa, residents described the air raids, which also struck the city's international airport, as "violent". Saudi state television reported earlier on Wednesday that the coalition had begun a military push against the Houthis after the group stepped up cross-border missile and drone attacks on the kingdom.
Nowadays it's hard to find a single industry where machine learning and data science aren't being used to improve productivity and deliver results. Indeed that is why people are so excited about the promise of artificial intelligence: it can be applied to so many diverse problem spaces effectively and it works! This list has been aggregated after analyzing over 200 company descriptions, and we've broadly organized them by the problem domain being tackled and have included a brief description of their mission. TLDR: A framework for providing data integrations and web interfaces for trained machine learning models. TLDR: Develops medical imaging tools powered by AI to help improve the efficacy of radiologists in detecting illnesses.
The Chinese new car market has been topsy turvy lately, primarily because the government keeps playing around with its NEV (new energy vehicle) incentive program. China really, really wants people to buy electric cars -- either plug-in hybrids or battery electrics -- but found its original incentive program was costing too much money. So it modified the program, several times in fact, which caused confusion among car companies and customers. In general, people who are confused postpone buying decisions until things get clearer, and that's exactly what Chinese new car shoppers did. The second factor, of course, was production shutdowns caused by the coronavirus pandemic.
All types of organizations are implementing AI projects for numerous applications in a wide range of industries. These applications include predictive analytics, pattern recognition systems, autonomous systems, conversational systems, hyper-personalization activities and goal-driven systems. Each of these projects has something in common: They're all predicated on an understanding of the business problem and that data and machine learning algorithms must be applied to the problem, resulting in a machine learning model that addresses the project's needs. Deploying and managing machine learning projects typically follow the same pattern. However, existing app development methodologies don't apply because AI projects are driven by data, not programming code.
Breed Reply, a European investor in early-stage Internet of Things (IoT) businesses, has increased its investment in Dutch agritech company, Connecterra. As part of its Series B funding round, Connecterra has secured €7.8 million from existing investors, Breed Reply and Sistema, alongside new investors including AgTech specialists ADM Capital, French food safety enterprise Kersia Group and Dutch impact investor, Pymwymic. The Series B funding round completed by Connecterra is the largest ever Series B investment raised by a European livestock tech company. The funding will be used to accelerate the development of Connecterra's predictive artificial intelligence (AI) platform, Ida. Ida is the first digital assistant for the dairy farmer, based on sensor technology, cloud computing and machine learning.
Whether or not your organisation suffers a cyber attack has long been considered a case of'when, not if', with cyber attacks having a huge impact on organisations. In 2018, 2.8 billion consumer data records were exposed in 342 breaches, ranging from credential stuffing to ransomware, at an estimated cost of more than $654bn. In 2019, this had increased to an exposure of 4.1 billion records. While the use of artificial intelligence (AI) and machine learning as a primary offensive tool in cyber attacks is not yet mainstream, its use and capabilities are growing and becoming more sophisticated. In time, cyber criminals will, inevitably, take advantage of AI, and such a move will increase threats to digital security and increase the volume and sophistication of cyber attacks.