Autopilot is the name for Tesla's (ticker: TSLA) basic driver-assistance functions. Most auto makers offer comparable features, such as adaptive cruise control and lane-keeping assistance. Tesla's Full-Self Driving, or FSD, system, available via a $10,000 upgrade, is a more advanced version of its driver-assistance features. The cameras combine with software that serves as the brains behind the vehicles' eyes. CEO Elon Musk said on the company's first-quarter earnings conference call in late April that the shift to an all-optical system, and developing its neural-network processing software, were critical elements to allowing cars to safely drive themselves in any setting.

By September 2021, the company is slated for implementation of their technology in the most tech- forward training centers in Singapore. Moreover, it begins shipping product to its consumer purchasers and coaches who are equally excited to get their hands on the technology. "This technology represents a jump forward for our training group, allowing previously unavailable movement data to influence our training decisions, all in a portable form factor," said Carl de Vries, founder of Athletic Inc., one of BODbx's earliest users. While in-depth information on the type of feedback and guidance a user receives has been notably kept close to the company's chest, it is clear that pose analysis allows for everything from measures of fatigue to specific details about how an individual's body should be moving in order to correct form. Expect to see calorie counts, reps, and other usual suspects after your workout, too, not to mention the daily workout optimization algorithm.

IBM is in the middle of a company transformation. With CEO Arvind Krishna and President Jim Whitehurst at the helm for a year, the company is going all-in on the cloud with its Red Hat and other investments and getting in a better posture for growth and focus with its recent Kyndryl spin-out. At the same time, the company continues to create leading edge research output in hardware, software and the cloud and creating some very innovative products simultaneously. IBM made many announcements prior to THINK. The company announced the world's first 2nm nanosheet device here, check out an interview I had with IBM Cloud leader Howard Boville here, see Z mainframe as a service here, check out Steve McDowell's analysis of Spectrum Fusion here, and see Paul Smith Goodson's coverage of Qiskit Metal for quantum computing here.

In many labs, urine cultures make up a significant part of the daily workload -- however, the majority of those samples have no growth or non-significant growth and don't require any additional work-up. Batch review of large volumes of plates with non-significant growth and release can help medical laboratorians focus their time and expertise on more clinically relevant tasks. "The use of the Urine Culture Application to automatically organize specimens into meaningful worklists driven by user-defined expert rules for critical criteria such as'high risk patients' or'complex specimens' will help laboratories work more efficiently and better prioritize their efforts to focus on the most critical and complex specimens," said Cecilia Soriano, vice president and general manager for Microbiology at BD Integrated Diagnostic Solutions. "Reading plates is one of the laboratory tasks that requires the most skill and given the decrease of skilled technicians entering the workforce and the strain on resources created by the COVID-19 pandemic, it's that much more critical for labs to be able to be more efficient." Powered by BD Synapsys informatics solution, the BD Kiestra Urine Culture application (UCA) may help improve lab efficiency and enhance quality by providing diagnostically relevant, standardized images for interpretation by laboratory staff.

ETSI has recently released ETSI GR SAI 005, a report which summarizes and analyses existing and potential mitigation against threats for AI-based systems. Setting a baseline for a common understanding of relevant AI cyber security threats and mitigations will be key for widespread deployment and acceptance of AI systems and applications. This report sheds light on the available methods for securing AI-based systems by mitigating known or potential security threats identified in the recent ENISA threat landscape publication and ETSI GR SAI 004 Problem Statement Report. It also addresses security capabilities, challenges, and limitations when adopting mitigation for AI-based systems in certain potential use cases. Artificial intelligence has been driven by the rapid progress of deep learning and its wide applications, such as image classification, object detection, speech recognition and language translation.

MONTREAL, March 16, 2021 /CNW/ - Amgen announced today that it has entered into a multi-year partnership with Mila – Quebec Artificial Intelligence Institute. One of the world's leading biotechnology companies, Amgen uses cutting-edge science and technology to discover and develop innovative therapeutics, and AI is currently deployed in several of its R&D and manufacturing activities. This new partnership will permit Amgen to expand its knowledge of AI and deep learning by interacting and engaging with experts in Mila's unique ecosystem, both virtually and (eventually) on the Mila campus in Montreal. This announcement was made during Effervescence 2021, an international virtual gathering of scientists, entrepreneurs and industry professionals from the life sciences and health technology sectors in the presence of the Quebec Minister of Economy and Innovation, Pierre Fitzgibbon. "We are very happy to have signed this partnership with Mila," said Philip Tagari, Amgen's Vice President of Research, Therapeutic Discovery.

Juniper Networks executives say the company's artificial-intelligence (AI) focused enterprise strategy is what is behind much of the positive momentum the company is experiencing. Speaking at the JP Morgan 49th Annual Global Technology, Media and Communications conference today, CEO Rami Rahim said that the company's enterprise business has never been as strong as it is today and he attributes much of that strength to the company's AI-driven enterprise strategy. "AI-driven enterprise is not just a marketing slogan," Rahim said. We have an AI engine that drives the solutions that we are offering customers today." Much of the company's AI-driven enterprise strategy is a result of its 2019 acquisition of Mist Systems, which had an AI-powered wireless platform that Juniper then used to enhance its own networking solutions.

Happiest Minds Technologies has recorded a whopping net profit of INR 36.05 crores– a 580.19% year on year growth -- in the latest quarter. The IT company's sales for the same period stood at INR 220.71 crore–an 18.44% spurt. Headquartered in Bengaluru and with offices in Pune and Delhi NCR, Happiest Minds Technologies helps businesses in their digital transformation journey. The company offers Internet of Things (IoT), analytics/artificial intelligence, and digital process automation services. The company has 194 clients as of March 31, 2021.

Frost & Sullivan's white paper highlights the process of creating an AI strategy for your imaging department The medical imaging industry is in the midst of large-scale transformation, largely driven by the advancements in technologies such as artificial intelligence (AI) and analytics, digitalization of radiology, as well as the rise of precision medicine and teleradiology. These cutting-edge technologies are aimed at addressing challenges in clinical, operational, and staff and patient experiences. In addition, they will resolve critical business pain points such as reimbursement cuts, sub-optimal use of imaging, radiologist burnout, missed appointments, and shortage of radiology subspecialists. Frost & Sullivan's latest white paper, A practical Guide for the Implementation of Artificial Intelligence in Medical Imaging, highlights the major challenges in imaging and explores how AI can be leveraged to solve these challenges. It also covers the likely pitfalls during the implementation process and some of the key factors to consider for the successful implementation of an AI program.

Use BigQuery to process tabular data. Use Dataflow to process unstructured data. Use managed datasets to link data to your models. The recommended approach for processing your data depends on the framework and data types you're using. This section provides high-level recommendations for common scenarios. For general recommendations on data engineering and feature engineering for ML, see Data preprocessing for machine learning: options and recommendations and Data preprocessing for machine learning using TensorFlow Transform. If you're using TensorFlow for model development, use TensorFlow Extended to prepare your data for training. TensorFlow Transform is the TensorFlow component that enables defining and executing a preprocessing function to transform your data.