In many respects, the future of work is already here. Amid the headlines exclaiming the predicted loss of jobs due to automation and other changes brought by artificial intelligence (AI), machine learning and autonomous systems, it's clear that the way we work and live is transforming. This evolution can be unnerving. Since we know change is inevitable, let's look at how work will likely change and some ideas for how to prepare for it. At least 30% of the activities associated with the majority of occupations in the United States could be automated, which includes even knowledge tasks that were previously thought to be safe according to a McKinsey Global Institute report.

The recent surveys, studies, forecasts and other quantitative assessments of the health and progress of AI highlighted the role of AI in cybersecurity defense and in scoring standardized tests, the relationships between data migrating to the cloud and data modernization, lax security standards for IoT devices, and that the U.S. still leads the global race for AI domination but that China is making more rapid progress. Written essays on standardized tests in 18 states in the U.S. are currently graded by natural language processing (NLP) software ("automated essay scoring engines") with only a small percentage of students' essays--it varies between 5% to 20%--randomly selected for a human grader to double check the machine's work [VICE] Based on a 100-point scale, the U.S. leads the global AI race with 44.2 points, followed by China with 32.3 points and the European Union with 23.5 points; The U.S. came out on top in talent, research, development, and hardware, while China led in adoption and data. And if you're someone who has a great idea but you have no data, that's going to be a big roadblock for you, and you're going to have to find some collaborators or partners who have access to the data you need"--Ben Wilson AI quote of the week: "The risk isn't that machines will get smarter.

A company statement said six Indian start-ups presented ideas that ranged from AI-enabled SaaS for rapid diagnosis of drug-resistant tuberculosis, to a next generation of light-based AI hardware. EF has helped over 600 co-founders build over 120 companies in Asia. In early 2019, EF announced the $115 million first close of a new global fund, one of the largest pre-seed venture capital funds ever raised. This will allow EF to invest in over2,200 individuals over the next three years.

Artificial intelligence (AI) has great potential and it helps you to gain large benefits in a competitively evolving market. AI has been driving business values significantly since the last few years. Organizations have realized the power of AI and they have started applying it to their businesses. Deploying AI and analytics to your business process will not only increase the efficiency of your operations, but also speed up the delivery and minimize the costs. Implementing AI for business operations will help you to recognize the most revenue generating customers and it can also attract them by rewarding them with the best suitable pricing.

We aren't alone in trying to move responsible AI from discussion to action while the technology is still in its infancy. The European Commission High-Level Expert Group on Artificial Intelligence and Singapore Personal Data Protection Commission also have independent initiatives underway. And the Montreal Declaration for Responsible Development of Artificial Intelligence and various industry-led or regional ethical AI projects are also addressing the issue. These are additional resources for associations willing to use their influence to ignite broad stakeholder adoption. Through conferences and education, associations can offer safe forums for thoughtful debate and practical planning around the fundamental choices we make for responsible AI. Associations stand at a tipping point of AI disruption. Industry and government stakeholders are looking for sensible guideposts for responsible conduct. Will you help define, model, and adopt responsible AI?

The incidence of construction workers' deaths in Israel has increased recently in 30% (annually). According to Workers' Hotline statistics, Israel is in third place among developed nations in terms of fatal construction accidents – 2.5 times that of the EU nations per capita. Despite their declarations of measures to be taken to deal with these accidents, it seems the authorities can't find effective solutions to the problem. Besides special training programs, legislation, increased enforcement and penalization, they should consider getting the startup nation involved in introducing advanced technologies to increase safety at construction sites and prevent these unnecessary accidents. The digital revolution we're in the midst of facilitates the application of technologies including autonomous equipment, drones and sensors that can monitor the safety of equipment, cranes and elevators, and even monitor wind speed and precipitation at the construction site.

Namita Pradhan sat at a desk in downtown Bhubaneswar, India, about 40 miles from the Bay of Bengal, staring at a video recorded in a hospital on the other side of the world. The video showed the inside of someone's colon. Pradhan was looking for polyps, small growths in the large intestine that could lead to cancer. When she found one -- they look a bit like a slimy, angry pimple -- she marked it with her computer mouse and keyboard, drawing a digital circle around the tiny bulge. She was not trained as a doctor, but she was helping to teach an artificial intelligence system that could eventually do the work of a doctor. Pradhan was one of dozens of young Indian women and men lined up at desks on the fourth floor of a small office building. They were trained to annotate all kinds of digital images, pinpointing everything from stop signs and pedestrians in street scenes to factories and oil tankers in satellite photos. AI, most people in the tech industry would tell you, is the future of their industry, and it is improving fast thanks to something called machine learning. But tech executives rarely discuss the labor-intensive process that goes into its creation.

The chief executive is headed to an artificial intelligence conference in Shanghai that kicks off on Thursday where he's set to face-off with Alibaba founder Jack Ma in what Bloomberg reports to be a "free-wheeling debate." "Conference-goers can look forward to featured speakers shedding light on the status quo of the industry and the potential impacts of artificial intelligence," a press release distributed by conference organizers says. Both leaders have been outspoken in their views on AI. Musk, who counts an AI company among his diverse portfolio of bets, has stressed the dangers of the nascent technology. "I'm really quite close, very close to the cutting edge in AI. It scares the hell out of me," Musk said at SXSW in 2018.

At PyBay 2019 in August, I will continue a conversation I started at PyBay 2018 about the importance of ethics in the artificial intelligence (AI) we're developing, especially as it gains more and more influence in our everyday lives. In last year's keynote, I dug into how we're overlooking the essential role humans play in AI's future. Ethical discussions around technology are more and more common, and I come to them from my first love, math. Math usually gives us a sense of certainty, but I have found that the more challenging, human parts of my work offer me the greatest potential to improve the world. If you're curious about the more technical side, here's a list of resources I put together: Misconceptions about the impact of all types and parts of technology have been common for a long time, but they are hitting home ever harder as AI systems gain increasing popularity and influence over our everyday lives.

UCLA engineers have made major improvements on their design of an optical neural network -a device inspired by how the human brain works - that can identify objects or process information at the speed of light. The development could lead to intelligent camera systems that figure out what they are seeing simply by the patterns of light that run through a 3D engineered material structure. Their new design takes advantage of the parallelization and scalability of optical-based computational systems. For example, such systems could be incorporated into self-driving cars or robots, helping them make near-instantaneous decisions faster and using less power than computer-based systems that need additional time to identify an object after it's been seen. The technology was first introduced by the UCLA group in 2018.