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Intertwining Machine Learning and APIs - DeepLobe


APIs (or Application Programming Interfaces) have been identified as important intermediaries between technologies like machine learning(ML) and their end-users. With big data streaming in vast data pools, organizations are turning towards machine learning APIs to leverage the technology and withdraw the complexities involved in creating and deploying machine learning models. APIs are making machine learning more consumable, scalable, and programmable. After machine learning's separation from statistics in the 1980s, the focus shifted towards inventing new algorithms and research on parameter estimation, scalability, and automation to establish it as a new technological advancement. But the main challenge was the fact that the development, usage, and implementation of the machine learning models were done by only tech geeks with domain knowledge.

Are Governments Ready For Artificial Intelligence? Role of AI in the Public Sector Post Pandemic


AI can improve populations' lives by providing better services in the following aspects: The COVID-19 crisis has sped up the adoption of artificial intelligence in the sector. With the on-going pandemic, governments are rethinking and reconfiguring their business models to navigate the uncertainties of the post COVID-19 world, they have started realising the potential of artificial intelligence to increase resilience, spot growth opportunities and drive innovation. Taken together, these benefits would equip public sector organizations to move beyond process optimization to deliver world class services and tackle long-term global challenges. Governments face particular barriers to deploying AI on a bigger scale. Not surprisingly, the historically low levels of IT investment in the public sector have slowed the introduction of AI in the public sector.

How do databases support AI algorithms?


The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Databases have always been able to do simple, clerical work like finding particular records that match some given criteria -- say, all users who are between 20 and 30 years old. Lately database companies have been adding artificial intelligence routines into databases so the users can explore the power of these smarter, more sophisticated algorithms on their own data stored in the database. The AI algorithms are also finding a home below the surface, where the AI routines help optimize internal tasks like re-indexing or query planning. These new features are often billed as adding automation because they relieve the user of housekeeping work.

Opinion: How artificial intelligence could save your life


Another AI-based living assistant provides pregnant women with guidance at various stages of pregnancy. The device acts as a communication platform for all of the people concerned and offers AI-informed advice. Such technology can raise pregnant women's awareness of the need to improve their self-care, especially in rural and remote areas where access to doctors and hospitals may be more limited. AI has also recently been recognized as one of the most accurate and reliable prediction systems. Health professionals can employ AI to precisely diagnose, manage, and predict different types of diseases at an early stage and estimate the patient's survival rate.

Government by algorithm: Can AI improve human decisionmaking?


Regulatory bodies around the world increasingly recognize that they need to regulate how governments use machine learning algorithms when making high-stakes decisions. This is a welcome development, but current approaches fall short. As regulators develop policies, they must consider how human decisionmakers interact with algorithms. If they do not, regulations will provide a false sense of security in governments adopting algorithms. In recent years, researchers and journalists have exposed how algorithmic systems used by courts, police, education departments, welfare agencies and other government bodies are rife with errors and biases.

Researchers develop artificial intelligence method to predict anti-cancer immunity


Researchers and data scientists at UT Southwestern Medical Center and MD Anderson Cancer Center have developed an artificial intelligence technique that can identify which cell surface peptides produced by cancer cells called neoantigens are recognized by the immune system. The pMTnet technique, detailed online in Nature Machine Intelligence, could lead to new ways to predict cancer prognosis and potential responsiveness to immunotherapies. "Determining which neoantigens bind to T cell receptors and which don't has seemed like an impossible feat. But with machine learning, we're making progress," said senior author Dr. Tao Wang, Ph.D., Assistant Professor of Population and Data Sciences, and with the Harold C. Simmons Comprehensive Cancer Center and the Center for Genetics of Host Defense at UT Southwestern. Mutations in the genome of cancer cells cause them to display different neoantigens on their surfaces.

Chris Pratt and Charlie Day headline the Mario Bros. movie in 2022


During Thursday's latest Nintendo Direct event, acclaimed video game designer Miyamoto Shigeru announced that the company's upcoming feature length animation project -- in conjunction with American film studio, Illumination -- now has a firm North American theatrical release date of December 21st, 2022. "Here we go!" Chris Pratt as Mario Anya Taylor-Joy as Peach Charlie Day as Luigi Jack Black as Bowser Keegan-Michael Key as Toad Seth Rogen as Donkey Kong Fred Armisen as Cranky Kong Kevin Michael Richardson as Kamek Sebastian Maniscalco as Spike Cameos from Charles Martinet While release dates for Europe, Japan, and other markets have yet to be revealed, Miyamoto did share the studio's key character casting decisions. Chris Pratt will voice Mario. "He's so cool," Miyamoto commented.

Deploying ML models to the edge with Lightning Flash


In this tutorial, we will package and deploy a simple model that exposes an HTTP API and serves predictions to a device managed by Synpse. Flash is a high-level deep learning framework for fast prototyping, baselining, finetuning and solving deep learning problems. It features a set of tasks for you to use for inference and finetuning out of the box, and an easy to implement API to customize every step of the process for full flexibility. Flash is built for beginners with a simple API that requires very little deep learning background, and for data scientists, Kagglers, applied ML practitioners and deep learning researchers that want a quick way to get a deep learning baseline with advanced features PyTorch Lightning offers. You can read more about the model in the image classification section.

AI luminary Kai-Fu Lee and sci-fi author Chen Qiufan predict the future in 'AI 2041' – TechCrunch


Where will today's technologies lead us over the next 20 years, and what will an AI-infused world look like across the globe? Sinovation founder and AI thought leader Kai-Fu Lee and breakout sci-fi author Chen Qiufan (aka Stanley Chen) make an educated guess in "AI 2041: Ten Visions for Our Future" a set of 10 stories and 10 essays exploring and explaining the potential and pitfalls of AI. After reading the book -- I'll be publishing a review shortly and TechCrunch recently posted an excerpt -- I talked with Lee and Chen at TechCrunch Disrupt 2021 about how the collaboration came about, how their points of view coincided and differed, and why they think the future will be how they describe it. Lee and Chen found each other a few years ago -- one a successful thought leader and entrepreneur in AI, the other an author whose incisive depictions of near-future dilemmas earned him international acclaim. They decided to collaborate on a hybrid work that would have narratives born out of informed speculation and expository pieces illustrated by narrative.