While this ban on technically manipulated videos of political figures isn't new and has been in place since the last presidential election in 2016, it illustrates just how increasingly difficult it is for the public (and organisations) to verify a person's true identity online. A deepfake today uses AI to combine existing imagery to replicate both their face and voice. Essentially, they can impersonate a real person, making them appear to say words they have never even spoken – hence the fear when it comes to general elections and politics being skewed by misinformed videos. Worryingly, the number of them online has doubled in less than a year, from 7,964 in December 2018 to more than 14,000 just nine months later. While the majority of these are porn-related, the problem isn't solely defined to this space.

Artificial intelligence (AI) is creating the fourth industrial revolution by shaping every single aspect in the world. AI is taking away jobs, and revolutionizing businesses only to create new human-driven creative opportunities. The new technology has influenced the way we drive cars, grow vegetables, detect cancer. It can even speak, and recognize emotions in speech. AI technology is disrupting right now all industries including digital marketing.

We're now accustomed to seeing robots deployed after natural disasters and in areas of heavy contamination. A robotics team, after all, was the quiet hero of the Fukushima disaster cleanup. Disruptive technologies like robotics and AI are working hard to fight the spread of the virus. In fact, the coronavirus outbreak is bringing renewed attention to an idea many in the robotics sphere have been trumpeting for some time: telemedicine. I reached out to ROBO Global, an index, advisory, and research company in the robotics sphere, for insight.

Human Bias is a serious issue in training datasets for Machine Learning and AI algorithms. The algorithms today are used in many critical situations and even in life-threatening scenarios, so limiting the bias is a requirement. Unbiased Data Marketplace address the issue of bias in training datasets with its unique approach and automation tools helping companies. In addition, Unbiased solution is transparent as all the events are recorded on the blockchain.

Even when taken simply as performance art rather than a potential solution, these designs can have a downside. Torin Monahan, a surveillance researcher at the University of North Carolina, says such projects risk leading people to believe that surveillance is inevitable and it's up to individuals to solve the problem. "These kinds of interventions tend to position surveillance as a universal threat to which individuals can respond and maybe should respond--but that misses how the affluent and white are positioned in a much more advantageous way than those who are marginalized and subject to police or state surveillance on a regular basis," he says. "I worry that by commercializing and aestheticizing surveillance in these ways, we aren't having a conversation about unequal vulnerabilities."

Chinese technology giant Alibaba recently developed an AI system for diagnosing the COVID-19 (coronavirus). Alibaba's like Amazon, Microsoft, a video game company, and a nation-wide healthcare network all rolled into one with every branch being fed solutions from the company's world-class AI department. Per a report from Nikkei's Asian Review (h/t TechSpot), Alibaba claims its new system can detect coronavirus in CT scans of patients' chests with 96% accuracy against viral pneumonia cases. And it only takes 20 seconds for the AI to make a determination – according to the report, humans generally take about 15 minutes to diagnose the illness as there can be upwards of 300 images to evaluate. The system was trained on images and data from 5,000 confirmed coronavirus cases and has already been tested in hospitals throughout China.

Data science and machine learning teams are now starting to be measured on business results rather than production metrics (the number of models produced, or projects started, for example). Consequently, the required disciplined approach brought about by commercial platforms is becoming a required condition to achieving business value and data science team sustainability.

An article published in medical journal The Lancet on February 25 finds that reduced medical resource levels will trigger a spike in the coronavirus death rate in the local population beyond the current estimates. The study shows that death rates are over 3 percent in Wuhan city, 2.9 percent in Hubei province, while only 0.7 percent across the rest of China. Close to 30,000 medical staff from across China have been dispatched to Hubei province to help overworked local medical professionals in the fight against COVID-19. Fast and accurate diagnosis is critical on the front line, and now an AI-powered diagnostic assessment system is helping Hubei medical teams do just that. Currently, CT lung scans and nucleic acid tests are the two main diagnostic tools doctors use in confirming COVID-19 infections.

Federal guidance on artificial intelligence needs additions to ensure the U.S. has a seat at the international table. The rapid proliferation of applications of artificial intelligence and machine learning--or AI, for short--coupled with the potential for significant societal impact has spurred calls around the world for new regulation. The European Union and China are developing their own rules, and the Organization for Economic Cooperation and Development has developed principles that enjoy the support of its members plus a handful of other countries. In January, the U.S. Office of Management and Budget (OMB) also issued its own draft guidance, ensuring the United States a seat at the table during this ongoing, multi-year, international conversation. The U.S. guidance--covering "weak" or narrow AI applications of the kind we experience today--reflects a light-touch approach to regulation, consistent with a desire to reward U.S. ingenuity.

Data has been hailed as the new gold, oil, and soil, out of which invaluable products, services, and progress shall arise. That may be true, but the fact of the matter is that data is just symbols, mostly numbers: zeros and ones. This applies even to the most complex algorithms, and most of what we mean when we talk about AI is classification software that assigns zeros or ones to match different variables or predict patterns at scale. To be sure, in most relevant areas of life, we still need human expertise to translate data into insights, and the willingness to act on those insights is what ultimately makes someone data-driven. Data without insights is meaningless, and insights without action are pointless.