How PostgreSQL accidentally became the ideal platform for IoT applications and services. From mainframes (1950s-1970s), to Personal Computers (1980s-1990s), to smartphones (2000s-now), each wave brought us smaller, yet more powerful machines, that were increasingly plentiful and pervasive throughout business and society. We are now sitting on the cusp of another inflection point, or major release if you will, with computing so small and so common that it is becoming nearly as pervading as the air we breathe. With each wave, software developers and businesses initially struggle to identify the appropriate software infrastructure on which to develop their applications. But soon common platforms emerge: Unix; Windows; the LAMP stack; iOS/Android.

Artificial intelligence (AI) is an innovation powerhouse. It autonomously learns on its own and evolves to meet simple and complex needs, from product recommendations to business predictions. As more people and services produce data, more powerful AI is necessary to process it all. AI chipsets that use edge computing are the solution. Cloud computing has been the leader for AI chipsets for years.

While smart cities and smart homes have become mainstream buzzwords, few people outside the IT and machine learning communities know about TensorFlow, PyTorch, or Theano. These are the open-source machine learning (ML) frameworks on which smart systems are built to integrate Internet of Things (IoT) devices among other things. ML algorithms and code are often found in publically available repositories, or data stores, that draw heavily on the aforementioned frameworks. In a December 2019 analysis of code hosting site GitHub, SMU Professor of Information Systems David Lo found over 46,000 repositories that were dependent on TensorFlow, and over 15,000 used PyTorch. Because of these frameworks' popularity, any vulnerability in them can be exposed to cause widespread damage.

Currency notes have identifiers that allow the visually impaired to identify them easily. This is a learned skill. On the other hand, classifying them using images is an easier solution to help the visually impaired identify the currency they are dealing with. Here, we use pictures of different versions of the currency notes taken from different angles, with different backgrounds and covering different proportions. The dataset contains 195 images of 7 categories of Indian Currency Notes -- Tennote, Fiftynote, Twentynote, 2Thousandnote, 2Hundrednote, Hundrednote, 1Hundrednote.

The past decade- the 2010s- was truly a decade of startups. Indeed, lots of successful startups are changing the world over the last 10 years. Analytics Jobs has brought you another story of a startup that enables Data Science and Artificial Intelligence to accelerate the discovery of drugs. The risks of cybersecurity are more advanced than ever before. Data is among the key features of every organization since it can help business leaders to make choices based on facts and figures, statistical numbers & trends.

"Instead of declaring a learning system's dynamics by implicit nonlinearities, we construct networks of linear first-order dynamical systems modulated via nonlinear interlinked gates. The resulting models represent dynamical systems with varying (i.e., liquid) time-constants coupled to their hidden state, with outputs being computed by numerical differential equation solvers. These neural networks exhibit stable and bounded behavior, yield superior expressivity within the family of neural ordinary differential equations, and give rise to improved performance on time-series prediction tasks." The Machine Learning Model Anonymization tool from IBM: "Traditional data anonymization algorithms don't consider the specific analysis the data is being used for. What if a 10-year range of ages is too general for an organization's needs? When these anonymization techniques are applied in the context of machine learning, they tend to significantly degrade the model's accuracy. The tool anonymizes machine learning models while being guided by the model itself. The method is agnostic to the specific learning algorithm and can be easily applied to any machine learning model, making it easy to integrate into existing MLOps pipelines."

The news: Facebook revealed a self-supervised artificial intelligence model it claims can accurately learn to categorize Instagram images with less human assistance than before. Here's how it works: Researchers at Facebook fed the AI, called SEER, over 1 billion unlabeled images extracted from public IG accounts. Using self-supervised learning--a method where a machine learns to train itself without human data labeling--SEER achieved a classification accuracy score of 84.2%, outperforming "the most advanced, state-of-the-art self-supervised systems," per Facebook. What's next?: While SEER is still in its early stages, Facebook believes it can bring about real-world benefits. Here are some of SEER's possible use cases: The bigger picture: Ever-increasing data sharing by users will likely lead to rapid AI advancement.

Artificial intelligence is the technological blow that took the world by storm. When the term'artificial intelligence' was first coined at a conference, no one imagined that one day, it will replace all the repetitive jobs and relieve humans from performing heavy labour works. The advent of the internet helped technology to progress exponentially. Artificial intelligence stood alone for the past three decades, and now, it is streamlining with widespread sub-technologies and applications. From biometrics and computer vision to smart devices and self-driving cars, emerging trends are fuelling the AI craze. Henceforth, Analytics Insight has listed the top 10 AI technologies that are taking innovation to next level in 2021.

With IBM's recent exploration to sell off its health business unit IBM Watson Health, The Wall Street Journal highlighted several issues with AI in healthcare that can hinder tech companies' innovation efforts. Despite spending several billion dollars on acquisitions to scale Watson Health, IBM's health business currently isn't profitable and is looking to sell, according to the Journal. IBM declined to comment on the sale, but offered the following statement to the publication about its successes over the past decade. "This work began nearly 10 years ago, at the beginning of the AI revolution, and we explored groundbreaking space in helping physicians advance healthcare through AI," the company said. "IBM is continuing to evolve the Watson Health business, based on our decade of experience, to meet the needs of patients and physicians."

JavaScript needs no special introduction -- it's one of the most popular cross-platform languages among web developers. And while some people consider it only a language for frontend development, JavaScript acts as an all-purpose programming language nowadays, and its possibilities are endless. Looking for the top JavaScript libraries that you can use in your Machine Learning projects? Synaptic is a well-known JavaScript neural network library created by MIT that can be used with Node.js or the browser. One significant feature of this library is its ability to build and train any first-order or second-order neural network architecture due to its architecture-free algorithm and pre-manufactured structure.