AI is developing at whirlwind rates. While nobody can say for certain how it will impact our work and personal lives, we can make a good few educated guesses. Also, with COVID-19 limiting human interaction in the built environment, advancements in AI and automation are on course to accelerate (providing funding is available, of course). The age-old fear among some of the population is that AI will displace workers, leading to high levels of unemployment. A report by management consulting firm McKinsey shows that between 400 million and 800 million individuals across the globe could be "replaced" by automation and need to find new jobs by 2030.

I've been working in the financial services space for close to 30 years now. I've seen many trends and technologies emerge. Some take hold, several are just a flash in the pan. Regardless of how long a concept sticks around, one thing remains: terminology plays a material role in shaping perceptions. In a world where messaging tends to over complicate things, too many acronyms and too many buzzwords all work against what should be the primary objective: clearly illustrating value.

"When you train algorithms on limited data, what you get is holes in the results." We are at the start of an epoch that will change everything, Stephanie Dinkins says about AI. But does the robots that we create represent everyone? In order to avoid problematic machine bias, she asks us developers to bring different people to the table. Developers shouldn't be afraid to rock the boat and encourage a little discomfort in the workplace.

A free facial recognition tool that allows people to find pictures of themselves or others from around the internet has drawn criticism from privacy campaigners. PimEyes describes itself as a privacy tool to help prevent misuse of images. But Big Brother Watch said it could "enable state surveillance, commercial monitoring and even stalking on a scale previously unimaginable". It comes as Amazon decides to pause its use of facial recognition for a year. Polish website PimEyes was set up in 2017 as a hobby project, and commercialised last year.

In this article, we will look at toxic speech detection, the problem of text moderation and understand the different challenges that one might encounter trying to automate the process. We look at several NLP and deep learning approaches to solve the problem and finally implement a toxic speech classifier using BERT embeddings. As of June 2019 there are now over 4.4 billion internet users. According to the latest Domo Data Never Sleeps report, Twitter users send 511,200 tweets per minute. While that happens, TikTok gets banned in Indonesia, Discord sees an increasing number of neo-Nazi posts, tech and film celebrity accounts get hacked so hackers can spurt out several racist slurs and hate speech volumes rise in India on facebook due to the controversial Citizenship Amendment Act (CAA). Social media continues to be used by several to incite violence, spread hate and target minorities based on religion, sex, race and disabilities.

As the world tries to grapple with the implications of 5G, researchers from China have already started looking into 6G. University, China, and others investigated the challenges of embracing 6G as the world moves towards ML heavy solutions. Their main objective is to find out how to make ML more feasible in a high-speed wireless environment. Federated learning, stated the authors, is an emerging distributed AI approach with privacy preservation nature is particularly attractive for various wireless applications, especially to achieve ubiquitous AI in 6G. Traditional Machine Learning techniques rely on a central server and are prone to critical security challenges, e.g., a single point of failure.

Data science is a combination of various machine learning principles along with tools and algorithms to analyze raw data and conclude hidden patterns or predictions. Data science does not only provide predictive casual analytics and perspective analytics but also machine learning for making predictions and pattern discovery. With these complex and meaningful analytics, it finds the critical insights out of anything that can help to enhance the value. There are a huge number of blogs that talk about all these data science projects and helps to enlighten its users about the new technology. Data science is an evergrowing field of computer science, and it is difficult to keep pace with the trendy additions all the time. The below-mentioned blogs of data science will help you to keep updated and stay ahead in the competition. After acquiring Datascence.com back in 2018, Oracle started focusing on the utilization of Machine learning for its customers. Oracle always wanted to enable people to leverage the power of AI with the combination of big data and data analytics. This big data blog can be seen as a part of this goal as it emphasizes the impact of big data and AI on various applications of our regular life. Besides, how we can transform the data catalog to get more insight from a business alongside the extraction of business value is discussed in Oracle AI and Data Science Blog. If you are planning to start your career in this field, you can follow this blog as you will get everything that you must understand to become a data scientist in 2020. This Belgium based data science community is publishing big data-related content to minimize the gap between data science and common people since 2015. The blogs are available for free, and you will get all of them in their archives. They are intended to generate solutions for the challenges that we face in our day-to-day life through data analytics. They are focused on educating and empowering people while the scholar and professionals are also included among their target audience. It can be seen as a bridge between academics and business as it highlights the power of big data and the value it can add to any business. NGO workers, business leaders, data enthusiasts, university professors, and also Ph.D. students share their skills and experiences through this blog.

Researchers at Brown University and Intel have proposed that they may be able to use artificial intelligence to help those with spinal cord injuries regain movement. The "Intelligent Spine Interface" project is a DARPA-backed initiative that proposes using AI to restore movement and bladder control in patients with paralyzing injuries. The 2-year program will involve capturing motor and sensory responses from the nervous system. Surgeons will then implant electrodes on the spinal cord above and below the injury called an "intelligent bypass." Intel-built neural networks with then use the data gathered to learn the signals for passing along motor responses through the bypass.

Washington – The Trump administration plans to reinterpret a Cold War-era arms agreement between 34 nations with the goal of allowing U.S. defense contractors to sell more American-made drones to a wide array of nations, three defense industry executives and a U.S. official told Reuters. The policy change, which has not been previously reported, could open up sales of armed U.S. drones to less stable governments such as Jordan and the United Arab Emirates that in the past have been forbidden from buying them under the 33-year-old Missile Technology Control Regime (MTCR), said the U.S. official, a former U.S. official and one of the executives. It could also undermine longstanding MTCR compliance from countries such as Russia, said the U.S. official, who has direct knowledge of the policy shift. Reinterpreting the MTCR is part of a broader Trump administration effort to sell more weapons overseas. It has overhauled a broad range of arms export regulations and removed the U.S. from international arms treaties including the Intermediate-Range Nuclear Forces Treaty and the Open Skies Treaty.

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