Britain has decided to allow Chinese tech giant Huawei to supply new high-speed network equipment, dealing a setback to the U.S. government and its global campaign to press allies into banning the company. The government's decision on Tuesday is the first by a major U.S. ally on the issue, which has seen intense lobbying from the Trump administration and China as the two vie for technological dominance. The British government said it is excluding "high risk" companies from supplying the sensitive "core" parts of the new fifth-generation, or 5G, networks. The core is the brain that keeps track, among other things, of smartphones connecting to networks and helps manage data traffic. But Britain will allow high risk suppliers to provide up to 35% of the less risky radio access network of antennas and base stations.

Atari is bringing its virtual experience to life with video game-themed hotels, and the first location will open in Phoenix. The first Atari Hotel is slated to break ground this year and open at an undetermined date. Atari Hotels also are planned for Las Vegas; Denver; Chicago; Seattle; San Francisco; Austin, Texas; and San Jose, California. The Phoenix hotel is still in its initial development stages, but the announcement holds a few hints as to what guests can expect. The hotel will offer "the ultimate in immersive entertainment and in every aspect of gaming," Shelly Murphy of GSD Group, one of the hotels' developers, said in a press release.

If you have tried to understand the maths behind machine learning, including deep learning, you would have come across topics from Information Theory – Entropy, Cross Entropy, KL Divergence, etc. The concepts from information theory is ever prevalent in the realm of machine learning, right from the splitting criteria of a Decision Tree to loss functions in Generative Adversarial Networks. If you are a beginner in Machine Learning, you might not have made an effort to go deep and understand the mathematics behind the ".fit()", but as you mature and stumble across more and more complex problems, it becomes essential to understand the math or at least the intuition behind the maths to effectively apply the right technique at the right place. When I was starting out, I was also guilty of the same. I'll see "Cross Categorical Entropy" as a loss function in a Neural Network and I take it for granted – that it is some magical loss function that works with multi-class labels. I'll see "entropy" as one of the splitting criterion in Decision Trees and I just experiment with it without understanding what it is. But as I matured, I decided to spend more time in understanding the basics and it helped me immensely in getting my intuitions right. This also helped in understanding the different ways the popular Deep Learning Frameworks, PyTorch and Tensorflow, have implemented the different loss functions and decide when to use what. This blog is me summarising my understanding of the underlying concepts of Information Theory and how the implementations differ across the different Deep Learning Frameworks.

If you have tried to understand the maths behind machine learning, including deep learning, you would have come across topics from Information Theory – Entropy, Cross Entropy, KL Divergence, etc. The concepts from information theory is ever prevalent in the realm of machine learning, right from the splitting criteria of a Decision Tree to loss functions in Generative Adversarial Networks. If you are a beginner in Machine Learning, you might not have made an effort to go deep and understand the mathematics behind the ".fit()", but as you mature and stumble across more and more complex problems, it becomes essential to understand the math or at least the intuition behind the maths to effectively apply the right technique at the right place. When I was starting out, I was also guilty of the same. I'll see "Cross Categorical Entropy" as a loss function in a Neural Network and I take it for granted – that it is some magical loss function that works with multi-class labels. I'll see "entropy" as one of the splitting criterion in Decision Trees and I just experiment with it without understanding what it is. But as I matured, I decided to spend more time in understanding the basics and it helped me immensely in getting my intuitions right. This also helped in understanding the different ways the popular Deep Learning Frameworks, PyTorch and Tensorflow, have implemented the different loss functions and decide when to use what. This blog is me summarising my understanding of the underlying concepts of Information Theory and how the implementations differ across the different Deep Learning Frameworks.

SANTA CLARA, Jan. 28, 2020 – ServiceNow (NYSE: NOW), the company making work, work better for people, today announced it has signed an agreement to acquire Passage AI, a Mountain View, Calif.–based conversational AI platform company. The transaction will advance ServiceNow's deep learning AI capabilities and will accelerate its vision of supporting all major languages across the company's Now Platform and products, including ServiceNow Virtual Agent, Service Portal, Workspaces and emerging interfaces. "Work flows more smoothly when people can get things done in their native language," said Debu Chatterjee, senior director of AI Engineering at ServiceNow. "Building deep learning, conversational AI capabilities into the Now Platform will enable a work request initiated in German or a customer inquiry initiated in Japanese to be solved by Virtual Agent. Passage AI's technology will enable us to accelerate our vision of empowering great employee and customer experiences by delivering great workflow experiences. ServiceNow believes in making work flow more smoothly across the enterprise, in all major languages."

If you want a machine to learn to do something intelligent you either have to program it or teach it to learn. For decades, engineers have been programming machines to perform all sorts of tasks -- from software that runs on your personal computer and smartphone to guidance control for space missions. But although computers are generally much faster and more precise than the human brain at sequential tasks, such as adding numbers or calculating chess moves, such programs are very limited in their scope. Something as trivial as identifying a bicycle among a crowded pedestrian street or picking up a hot cup of coffee from a desk and gently moving it to the mouth can send a computer into convulsions, nevermind conceptualizing or abstraction (such as designing a computer itself). The gist is that humans were never programmed (not like a digital computer, at least) -- humans have become intelligent through learning.

Artificial intelligence (AI) is penetrating every department of every industry, from automating factory work to improving areas previously thought untouchable by machines (like human resources). But as a veteran in the online marketing world, I can't help but let my imagination wander on how AI and machine learning are going to impact the world of search engine optimization (SEO)--the strategies organizations use to rank higher in search engine results pages (SERPs). Already, we're seeing the beginnings of a full-scale AI revolution in SEO, and search marketers are scrambling to keep pace with the changes. But what will the next few years bring? We say "search engines," but most of the time, we're talking about Google. Bing, Yahoo!, DuckDuckGo, and other engines only share a fraction of the search user base, and most of their systems are modeled after Google's in the first place.

Artificial intelligence (AI) is penetrating every department of every industry, from automating factory work to improving areas previously thought untouchable by machines (like human resources). But as a veteran in the online marketing world, I can't help but let my imagination wander on how AI and machine learning are going to impact the world of search engine optimization (SEO)--the strategies organizations use to rank higher in search engine results pages (SERPs). Already, we're seeing the beginnings of a full-scale AI revolution in SEO, and search marketers are scrambling to keep pace with the changes. But what will the next few years bring? We say "search engines," but most of the time, we're talking about Google. Bing, Yahoo!, DuckDuckGo, and other engines only share a fraction of the search user base, and most of their systems are modeled after Google's in the first place.

Like the double helix of DNA, innovation and economic productivity are complementary and inextricably linked. Crucial technological developments such as the steam engine and the internet preceded the Industrial Revolutions of the past, and the same might be said of the Fourth Industrial Revolution that is unravelling in the present. Fundamentally, innovation brings new tools to bear for industries to enhance their productivity. In the latest expansion of the toolkit, artificial intelligence (AI) is becoming one of the most sought-after technologies by companies around the world. According to a Gartner report, the number of enterprises actively pursuing AI implementation grew by 270 percent in the last four years.