Artificial intelligence and machine learning (AI/ML) systems are growing exponentially around the world and is estimated to generate AU$22.17 trillion to the global economy by 2030. The Australian Government's Artificial Intelligence Technology Roadmap, developed by Data61, identified Australia's need for up to 161,000 new specialist AI workers by 2030. Stuart Ayres, NSW Minister for Jobs, Investment, Tourism and Western Sydney, said: "NeurIPS 2021 will propel Australia's research and innovative discoveries to the forefront – bringing with it opportunities for trade and investment and talent attraction as well as helping to build Sydney's brand as an intellectual capital." Dr. Terrence Sejnowski, President of the NeurIPS Foundation, agreed that it was a "significant step" bringing the conference to Australia. It will be the first time NeurIPS is held in the Asia-Pacific region, and only the third time it has been held outside North America.

Together with the rise of the Internet, access to large repositories of data has helped machine learning technology grow exponentially. The incredibly quick pace of growth was unprecedented. As a result, it is obvious that AI will make a significant impact on the world in the years to come. However, with the numerous established and emerging fields of AI around today, such a blanket statement doesn't provide much concrete meaning. What fields and applications of AI are receiving the most investment and development?

The Trump administration is proposing new rules to guide future federal regulation of artificial intelligence used in medicine, transportation and other industries. But the vagueness of the principles announced by the White House is unlikely to satisfy AI watchdogs who have warned of a lack of accountability as computer systems are deployed to take on human roles in high-risk social settings, such as mortgage lending or job recruitment. A document from the White House said that in deciding regulatory action, U.S. agencies "must consider fairness, non-discrimination, openness, transparency, safety, and security." The rules won't affect how federal agencies such as law enforcement use AI; they are specifically limited to how federal agencies devise new AI regulations for the private sector. There's a month-long public comment period before the rules take effect.

The Trump administration is proposing new rules to guide future federal regulation of artificial intelligence used in medicine, transportation and other industries. But the vagueness of the principles announced by the White House is unlikely to satisfy AI watchdogs who have warned of a lack of accountability as computer systems are deployed to take on human roles in high-risk social settings, such as mortgage lending or job recruitment. The White House said that in deciding regulatory action, U.S. agencies "must consider fairness, non-discrimination, openness, transparency, safety, and security." But federal agencies must also avoid setting up restrictions that "needlessly hamper AI innovation and growth," reads a memo being sent to U.S. agency chiefs from Russell Vought, acting director of the Office of Management and Budget. "Agencies must avoid a precautionary approach that holds AI systems to such an impossibly high standard that society cannot enjoy their benefits," the memo says.

AI has the capacity to make decisions in real-time, based on pre-installed algorithms and efficient computing technologies. With an HR department encompassing the human element and AI, companies can provide an enhanced experience for their candidates and employees, writes Khalid Durrani, Digital Marketing Manager, Cubix. "Deep-learning will transform every single industry," said Andrew Ng, a Chinese-American scientist excelling in machine learning and AI. McKinsey's forecast on machine learning backs up his statement claiming that by 2030, AI will have a significant impact of $13 trillion on the global economy. HR professionals understand the importance of optimizing the combination of the human mind and machine learning for a seamless workflow and intuitive work environment.

The chip designer Ambarella has announced two new chips for automotive cameras and advanced driver assistance systems (ADAS) based on its CVflow architecture for artificial intelligence processing. The Santa Clara, California-based company introduced the CV22FS and CV2FS automotive camera (SoC) systems with CVflow AI processing and ASIL-B compliance to enable critical safety applications. Ambarella will also demonstrate applications with its existing chips, as well as a robotic platform and Amazon SageMaker Neo technology to train machine learning models, at CES 2020, the big technology fair in Las Vegas this week. The company, which was made public in 2011, started as a manufacturer of low-power chips for video cameras. But he turned that ability into computer vision experience and launched his CVflow architecture in 2018 to create low-power artificial intelligence chips.

MIT researchers have built a new benchmark tool that can accurately predict how long it takes given code to execute on a computer chip, which can help programmers tweak the code for better performance. MIT researchers have invented a machine-learning tool that predicts how fast computer chips will execute code from various applications. To get code to run as fast as possible, developers and compilers -- programs that translate programming language into machine-readable code -- typically use performance models that run the code through a simulation of given chip architectures. Compilers use that information to automatically optimize code, and developers use it to tackle performance bottlenecks on the microprocessors that will run it. But performance models for machine code are handwritten by a relatively small group of experts and are not properly validated.

BUSINESSES often think that machine learning (ML) models learn on their own and get better over time. If organizations want to use the technology effectively in 2020, they need to understand why and what to do about it. Business leaders have been told that they need a mountain of data to train any artificial intelligence (AI) or machine learning model. As a result, much of their efforts in the past year have been focused on acquiring data. However, once the models are deployed, they stop evolving and fail to account for changes that occur in variables.

It's hard to find a tech company that isn't attempting some sort of AI-related product, service, or initiative these days, but eBay went all-in by building its own AI platform, called Krylov. Sanjeev Katariya, eBay's VP and chief architect of AI and platforms, described Krylov in an interview with VentureBeat: "At the very highest level, Krylov is a machine learning platform that enables data scientists and machine learning engineers to ship all different kinds of models for all kinds of data quickly into production, which gets integrated into user experiences that eBay ships globally." It's a multi-tenant, cloud-based platform that involves technologies like computer vision and natural language processing (NLP), techniques including distributed training and hyper-parameter tuning, and tools germane to eBay's services, like merchandising recommendations, buyer personalization, seller price guidance, and shipping estimates. Even if they did, the hard costs -- however significant they may or may not be -- wouldn't fully capture what eBay has invested to build the platform over years of internal organizational efforts around the globe. And after all that, eBay is now open-sourcing Krylov.

Despite investment, research publications and job demand in the field continuing to grow through 2019, technologists are starting to come to terms with potential limitations in what AI can realistically achieve. Meanwhile, a growing movement is grappling with its ethics and social implications, and widespread business adoption remains stubbornly low. As a result, companies and organizations are increasingly pushing tools that commoditize existing predictive and image recognition machine learning, making the tech easier to explain and use for non-coders. Emerging breakthroughs, like the ability to create synthetic data and open-source language processors that require less training than ever, are aiding these efforts. At the same time, the use of AI for nefarious ends like deepfakes and the mass-production of spam are still in their earliest theoretical stages, and troubling reports indicate such dystopia may become more real in 2020.