Johnson & Johnson (J&J) is an American multinational corporation founded in 1886 that develops medical devices, pharmaceuticals, and consumer packaged goods. Its common stock is a component of the Dow Jones Industrial Average and the company is ranked No. 36 on the 2021 Fortune 500 list of the largest United States corporations by total revenue.

In May, Twitter said that it would stop using an artificial intelligence algorithm found to favor white and female faces when auto-cropping images. Now, an unusual contest to scrutinize an AI program for misbehavior has found that the same algorithm, which identifies the most important areas of an image, also discriminates by age and weight, and favors text in English and other Western languages. The top entry, contributed by Bogdan Kulynych, a graduate student in computer security at EPFL in Switzerland, shows how Twitter's image-cropping algorithm favors thinner and younger-looking people. Kulynych used a deepfake technique to auto-generate different faces, and then tested the cropping algorithm to see how it responded. "Basically, the more thin, young, and female an image is, the more it's going to be favored," says Patrick Hall, principal scientist at BNH, a company that does AI consulting.

Yang Liu, an Alexa AI principal scientist, and Ruhi Sarikaya, director of applied science, Alexa AI, have recently been named IEEE Fellows. The designation takes effect January 1, 2021. Liu is being honored for her "contributions to speech understanding and language-learning technology", while Sarikaya is being recognized for his "leadership in spoken language processing, and conversational understanding systems". Both currently lead research initiatives focused on making Alexa more natural and conversational, perceptive and context aware, and capable of self learning. The IEEE Fellow designation is conferred by the IEEE board of directors upon individuals with outstanding records of accomplishment in any of the IEEE fields of interest.

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Artificial intelligence jobs have been hot in Silicon Valley and elsewhere, with a machine-learning engineer getting an average salary of $142,858 a year. But AI job-posting growth has slowed, and interest in these jobs is also dipping, according to a study by job site Indeed. AI job postings on Indeed rose 29.1% from May 2018 to May 2019. However, that increase is substantially less than it was the previous two years. During the same time period -- May 2017 to May 2018 -- AI job postings on Indeed rose 57.9%, and a whopping 136.2% between May 2016 and May 2017.

At UCB, we put our heart, soul and skills into making a difference for people living with chronic disease. Working together to push the boundaries, we blend the best of our talents to unlock innovation. We are seeking a top class candidate to join our Computer-Aided Drug Design (CADD) team within Global Chemistry to drive the application of artificial intelligence and advanced machine learning technologies in small molecule drug discovery. The successful candidate will have solid understanding of small molecule drug discovery, computer-aided drug design, and applications of AI and advanced machine learning algorithms to address challenges in chemistry. This position can be located either in our UK offices in Slough or in our Belgian offices in Braine-l'Alleud.

Last month, Amazon Alexa AI science leader Young-Bum Kim wrote a blog post about a paper he's presenting at the annual conference of the Association for Computational Linguistics (ACL), which describes the first part of the two-part system that will enable Alexa to select the one skill out of thousands that's best suited to a particular customer request. He described the second part in a paper presented at a meeting of the North American chapter of the ACL, which was under way at the time. ACL begins in Melbourne, Australia on July 15, and in addition to Kim, two other Amazon AI researchers are coauthors of papers accepted to the conference. Anima Anandkumar, a principal scientist in Amazon's Rekognition and Video group, and colleagues at Cornell University will present a new technique for producing word embeddings, which attempt to mathematically capture words' semantic similarities. And Alessandro Moschitti, a principal scientist in Amazon's Machine Translation/Natural Language Processing group, is coauthor on a pair of papers that use innovative machine-learning systems for classifying discussion-forum threads as a springboard for addressing some more general problems.

Futurewei Technologies Inc. (DBA Huawei R&D) is a U.S. Based branch of Huawei Technologies Co. Ltd., the leading global information and communications technology solutions provider. Established in 1988, Huawei Technologies is a private high-tech enterprise specializing in research and development, the production and marketing of communications equipment, and providing customized network solutions for telecom carriers in fixed, mobile, and data communications networks. Through our dedication to customer-centric innovation and strong partnerships, we have established end-to-end advantages in telecom networks, devices and cloud computing. We are committed to creating maximum value for telecom operators, enterprises and consumers by providing competitive solutions and services. Our products and solutions have been deployed in over 170 countries, serving more than one third of the world's population.

In this talk Blaise Agüera y Arcas, Principal Scientist at Google, will begin by taking a close look at what has been achieved in machine intelligence (especially deep learning) over the past decade, with examples of current techniques and applications from the Machine Intelligence on Devices group at Google and from the community. This includes not only classification and semantic understanding of natural stimuli, but also language, gameplay, and even art. From here Blaise will zoom out and consider some broader questions about human progress, labour and identity in an era of "technological reproducibility". Blaise Agüera y Arcas is Principal Scientist at Google where he leads a team focusing on Machine Intelligence for mobile devices - including both basic research and new products. His group works extensively with deep neural nets for machine perception, distributed learning, and agents, as well as collaborating with academic institutions on connectomics research.