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Best Machine Learning Blogs to Follow in 2021

#artificialintelligence

Towards AI is a community that discusses artificial intelligence, data science, data visualization, deep learning, machine learning, NLP, computer vision, related news, robotics, self-driving cars, programming, technology, and more! From researchers to students, industry experts, and machine learning (ML) enthusiasts -- keeping up with the best and the latest machine learning research is a matter of finding reliable sources of scientific work. While blogs usually update in a more informal and conversational style, we have found that the sources in this list are accurate, resourceful, and reliable sources of machine learning research. Please know that the blogs listed below are by no means ranked or in a particular order. They are all incredible sources of machine learning research.


IBM is using light, instead of electricity, to create ultra-fast computing

ZDNet

To quench algorithms' seemingly limitless thirst for processing power, IBM researchers have unveiled a new approach that could mean big changes for deep-learning applications: processors that perform computations entirely with light, rather than electricity. The researchers have created a photonic tensor core that, based on the properties of light particles, is capable of processing data at unprecedented speeds, to deliver AI applications with ultra-low latency. Although the device has only been tested at a small scale, the report suggests that as the processor develops, it could achieve one thousand trillion multiply-accumulate (MAC) operations per second and per square-millimeter – more than twice as many, according to the scientists, as "state-of-the-art AI processors" that rely on electrical signals. IBM has been working on novel approaches to processing units for a number of years now. Part of the company's research has focused on developing in-memory computing technologies, in which memory and processing co-exist in some form.


Top 100 Artificial Intelligence Companies in the World

#artificialintelligence

Artificial Intelligence (AI) is not just a buzzword, but a crucial part of the technology landscape. AI is changing every industry and business function, which results in increased interest in its applications, subdomains and related fields. This makes AI companies the top leaders driving the technology swift. AI helps us to optimise and automate crucial business processes, gather essential data and transform the world, one step at a time. From Google and Amazon to Apple and Microsoft, every major tech company is dedicating resources to breakthroughs in artificial intelligence. As big enterprises are busy acquiring or merging with other emerging inventions, small AI companies are also working hard to develop their own intelligent technology and services. By leveraging artificial intelligence, organizations get an innovative edge in the digital age. AI consults are also working to provide companies with expertise that can help them grow. In this digital era, AI is also a significant place for investment. AI companies are constantly developing the latest products to provide the simplest solutions. Henceforth, Analytics Insight brings you the list of top 100 AI companies that are leading the technology drive towards a better tomorrow. AEye develops advanced vision hardware, software, and algorithms that act as the eyes and visual cortex of autonomous vehicles. AEye is an artificial perception pioneer and creator of iDAR, a new form of intelligent data collection that acts as the eyes and visual cortex of autonomous vehicles. Since its demonstration of its solid state LiDAR scanner in 2013, AEye has pioneered breakthroughs in intelligent sensing. Their mission was to acquire the most information with the fewest ones and zeros. This would allow AEye to drive the automotive industry into the next realm of autonomy. Algorithmia invented the AI Layer.


Interactive Visualization System that Helps Students Better Understand and Learn CNNs

#artificialintelligence

This research summary is just one of many that are distributed weekly on the AI scholar newsletter. To start receiving the weekly newsletter, sign up here. Artificial intelligence (AI) has grown tremendously in just a few years ushering us into the AI era. We now have self-driving cars, contemporary chatbots, high-end robots, recommender systems, advanced diagnostics systems, and more. Almost every research field is now using AI.


Amazon's Chief Technology Officer Shares Predictions for 2021

WSJ.com: WSJD - Technology

"I tried to stay with some of the things that I know will be happening because we have some control of them," said Mr. Vogels. On Wednesday, he shared eight predictions based on customer-behavior patterns and technology investments by the company. The cloud will be everywhere. Next year will see more devices and more organizations powered by the cloud. Mr. Vogel, whose expertise in scalable systems led him to Amazon.com in 2004, predicts that the cloud in 2021 will continue to move beyond the traditional notion of a centralized system, with troves of data moving back and forth between customers and massive data centers in real time.


Top Datasets of 2020

#artificialintelligence

I am sure everyone can attest to this saying. No matter what your task is, practice makes you better at it. In my Machine Learning journey, I have observed nothing different. In fact, I would go so far as to say that understanding a model itself, say, Logistic regression is less challenging than understanding where it should be applied as its application differs from dataset to dataset. Therefore, it is highly important that we practice the end-to-end Machine Learning process on different kinds of data and datasets. The more diverse datasets we use to build our models, the more we understand the model. This is also a great way to keep challenging yourself and explore some interesting data being collected around the world!


Top 20 Predictions Of How AI Is Going To Improve Cybersecurity In 2021

#artificialintelligence

Gartner's latest Information Security and Risk Management forecast predicts the market will achieve ... [ ] an 8.3% Compound Annual Growth Rate (CAGR) growth rate from 2019 through 2024, reaching $211.4 billion. Bottom Line: In 2021, cybersecurity vendors will accelerate AI and machine learning app development to combine human and machine insights so they can out-innovate attackers intent on escalating an AI-based arms race. Attackers and cybercriminals capitalized on the chaotic year by attempting to breach a record number of enterprise systems in e-commerce, financial services, healthcare and many other industries. AI and machine learning-based cybersecurity apps and platforms combined with human expertise and insights make it more challenging for attackers to succeed in their efforts. Accustomed to endpoint security systems that rely on passwords alone, admin accounts that don't have fundamental security in place, including Multi-Factor Authentication (MFA) and more and attackers created a digital pandemic this year. Interested in what the leading cybersecurity experts are thinking will happen in 2021, I contacted twenty of them who are actively researching how AI can improve cybersecurity next year. Leading experts in the field include including Nicko van Someren, Ph.D. and Chief Technology Officer at Absolute Software, BJ Jenkins, President and CEO of Barracuda Networks, Ali Siddiqui, Chief Product Officer and Ram Chakravarti, Chief Technology Officer, both from BMC, Dr. Torsten George, Cybersecurity Evangelist at Centrify, Tej Redkar, Chief Product Officer at LogicMonitor, Bill Harrod, Vice President of Public Sector at Ivanti, Dr. Mike Lloyd, CTO at RedSeal and many others.



Data Science Course 2021: Complete Machine Learning Training

#artificialintelligence

" We will shift from a mobile first to an AI first world." Artificial intelligence (AI) is one of the most important technologies of the 21st century and part of the 4th industrial revolution. AI will transform every industry similar to electricity over 100 years ago and have a huge impact on how humans live and work in the future. Moving into Data Science is an amazing career choice. There's high demand for Data Scientists across the globe and people working in the field enjoy high salaries and rewarding careers.


Developing Future Human-Centered Smart Cities: Critical Analysis of Smart City Security, Interpretability, and Ethical Challenges

arXiv.org Artificial Intelligence

As we make tremendous advances in machine learning and artificial intelligence technosciences, there is a renewed understanding in the AI community that we must ensure that humans being are at the center of our deliberations so that we don't end in technology-induced dystopias. As strongly argued by Green in his book Smart Enough City, the incorporation of technology in city environs does not automatically translate into prosperity, wellbeing, urban livability, or social justice. There is a great need to deliberate on the future of the cities worth living and designing. There are philosophical and ethical questions involved along with various challenges that relate to the security, safety, and interpretability of AI algorithms that will form the technological bedrock of future cities. Several research institutes on human centered AI have been established at top international universities. Globally there are calls for technology to be made more humane and human-compatible. For example, Stuart Russell has a book called Human Compatible AI. The Center for Humane Technology advocates for regulators and technology companies to avoid business models and product features that contribute to social problems such as extremism, polarization, misinformation, and Internet addiction. In this paper, we analyze and explore key challenges including security, robustness, interpretability, and ethical challenges to a successful deployment of AI or ML in human-centric applications, with a particular emphasis on the convergence of these challenges. We provide a detailed review of existing literature on these key challenges and analyze how one of these challenges may lead to others or help in solving other challenges. The paper also advises on the current limitations, pitfalls, and future directions of research in these domains, and how it can fill the current gaps and lead to better solutions.