In this article, we are going to discuss we difference between Artificial Intelligence, Machine Learning, and Deep Learning. Furthermore, we will address the question of why Deep Learning as a young emerging field is far superior to traditional Machine Learning. Artificial Intelligence, Machine Learning, and Deep Learning are popular buzzwords that everyone seems to use nowadays. But still, there is a big misconception among many people about the meaning of these terms. In the worst case, one may think that these terms describe the same thing -- which is simply false.

The novel coronavirus that first appeared in mainland China has now spread across the world, with more than 82,000 reported cases and nearly 3,000 deaths, as of Thursday. And right alongside the outbreak is the deployment of myriad types of AI-powered tech that is now being put on full display. New technology like infrared thermometers -- potentially unreliable devices known as "thermometer guns" -- are becoming increasingly commonplace in China, where health workers regularly check people's temperatures. Somewhat behind the scenes, however, more futuristic technology powered by artificial intelligence is helping to identify coronavirus symptoms, find new treatments, and track the spread of the disease. Meanwhile, robots are making interactions with and treatment of sick patients easier. Powerful surveillance tech -- including facial recognition-enabled cameras and drones -- is also helping find people who might be sick or who aren't wearing masks.

Data from Strategy Analytics show that smart speaker shipments in the Nordic countries of Denmark, Norway, and Sweden reached 900,000 in 2019. That figure is up sharply from shipments of only about 200,000 in 2018. These figures indicate impressive growth and smart speaker interest in countries that collectively claim only about 20 million in population. Strategy Analytics estimates the household installed base for smart speakers these Nordic countries is about 6%. Finland and Iceland were not included in the analysis since none of the leading smart speaker makers have offerings with language localization for these countries. Google Home (a.k.a Google Nest) dominates smart speaker market share in the Nordic countries accounting for over 80% of all devices shipped according to David Watkins, director of connected home devices at Strategy Analytics.

ETL stands for Extract Transform Load, which is a crucial procedure in the process of data preparation. With the help of ETL, one can easily access data from various interfaces. This means it can collect and migrate data from various data structures across various platforms. In this article, we list down 10 Python-Based top ETL tools. Apache Airflow is a Python-based workflow automation tool, which can be used to author workflows as Directed Acyclic Graphs (DAGs) of tasks.

Stuart Russell is a distinguished artificial intelligence researcher, a Professor of Computer Science at the University of California, Berkeley, an Adjunct Professor of Neurological Surgery at the University of California, San Francisco, and leads the Center for Human-Compatible Artificial Intelligence at UC Berkeley. Along with Peter Norvig, Stuart is the author of Artificial Intelligence: A Modern Approach, the most widely used textbook on artificial intelligence. In his most recent book, Human Compatible: AI and the Problem of Control, Stuart proposes a fundamentally new approach to developing AI. In this interview, Stuart warns that AI is reshaping society in unintended ways. For example, social media content selection algorithms that choose what individuals watch and read do not even know that human beings exist. As AI becomes more capable, he suggests that we are going to see bigger failures unless we change the way we think about AI altogether. Stuart argues that to ensure AI is provably beneficial for human beings, we must design machines to be inherently uncertain about human preferences. This way, we can ensure they are humble, altruistic, and committed to pursuing our objectives even as they set their own goals. We also discuss why AI needs regulation similar to civil engineering and medicine, the impact AI is going to make over the next decade, autonomous vehicles, and a variety of other topics.

Chengdu, China - Sitting at the entrance of Chengdu's East Railway Station, Fu Guobin stared at a screen displaying infrared images of people passing through the station's gates. As each person entered, a number popped up next to their image indicating their body temperature. "This is making my life much easier," the station employee said as he sat in his booth. "Before this, I'd have to test everyone's temperature with an ear thermometer. And sometimes that doesn't work - I think this new system is much better."

More than 50 years ago Marvin Minsky made the first attempt to mimic the human brain, triggering further research into computers' ability to process information to make intelligent decisions. Over the years, the process of automating image analysis led to the programming of algorithms. However, it was only from 2010 onward, when there was acceleration in deep learning techniques. In 2012 Google Brain built a neural network of 16,000 computer processors which could recognise pictures of cats using a deep learning algorithm. This is an edited extract from the Computer Vision – Thematic Research report produced by GlobalData Thematic Research.

The world of artificial intelligence is constantly evolving, and certainly so is the legal and regulatory environment in which it exists. Michael Hayes, Senior Manager of Government Affairs at the Consumer Technology Association (CTA) is focused on these emerging technology challenges that hit up against existing laws and regulations. Michael previously worked on Capitol Hill on patent reform, stopping patent trolls. As part of his current role, Michael makes sure that the emerging policy discussion is framed in a way that makes sure that the technology can thrive and provide competitive advantages for companies implementing them without introducing new risks. There has been a lot of concern about corporation and government's use of data, and the role of privacy.

In the year 2018 Facebook disclosed a massive data breach due to which the company had to face a lawsuit along with allegations of not properly securing its user data. The breach directly affected the authentication tokens of nearly 30 million of its users which led to the filing of several class-action complaints in a San Francisco appeals court. In the wake of the incident, Facebook pledged to strengthen its security. A feature, known as "View As" which was employed by developers to render user pages was exploited by hackers to get access to user tokens. The theft of these tokens is associated with the advancement of a major API security risk, it also indicates how API risks can go unnoticed for such a long time frame.

A robot that mimics the behaviours of people who have obsessive-compulsive disorder (OCD) could help us understand what drives the condition and even improve how we treat it. OCD involves obsessive worrying that compels people to carry out rituals like repeated hand washing. People who have it experience anxiety if they can't complete these compulsions. Researchers are using the robot to recreate this compulsive drive to complete an action.