When one technology replaces another, its not easy to accurately ascertain how the new technology would impact our lives. With so much buzz around the modern applications of Artificial Intelligence, Machine Learning, and Data Science, it becomes difficult to track the developments of these technologies. Machine Learning, in particular, has undergone a remarkable evolution in recent years. Many Machine Learning (ML) techniques have come in the foreground recently, most of which go beyond the traditionally simple classifications of this highly scientific Data Science specialization.

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Lets point out the top ML techniques that the industry leaders and investors are keenly following, their definition, and commercial application.

Perceptual Learning is the scientific technique of enabling AI ML algorithms with better perception abilities to categorize and differentiate spatial and temporal patterns in the physical world.

For humans, Perceptual Learning is mostly instinctive and condition-driven. It means humans learn perceptual skills without actual awareness. In the case of machines, these learning skills are mapped implicitly using sensors, mechanoreceptors, and connected intelligent machines.

Most AI ML engineering companies boast of developing and delivering AI ML models that run on an automated platform. They openly challenge the presence and need for a Data Scientist in the Engineering process.

Automated Machine Learning (AutoML) is defined as the fully automating the entire process of Machine Learning model development right up till the process of its application.

AutoML enables companies to leverage AI ML models in an automated environment without truly seeking the involvement and supervision of Data Scientists, AI Engineers or Analysts.

Google, Baidu, IBM, Amazon, H2O, and a bunch of other technology-innovation companies already offer a host of AutoML environment for many commercial applications. These applications have swept into every possible business in every industry, including in Healthcare, Manufacturing, FinTech, Marketing and Sales, Retail, Sports and more.

Bayesian Machine Learning is a unique specialization within AI ML projects that leverage statistical models along with Data Science techniques. Any ML technique that uses the Bayes Theorem and Bayesian statistical modeling approach in Machine Learning fall under the purview of Bayesian Machine Learning.

The contemporary applications of Bayesian ML involves the use of open-source coding platform Python. Unique applications include

A good ML program would be expected to perpetually learn to perform a set of complex tasks. This learning mechanism is understood from the specialized branch of AI ML techniques, called Meta-Learning.

The industry-wide definition for Meta-Learning is the ability to learn and generalize AI into different real-world scenarios encountered during the ML training time, using specific volume and variety of data.

Meta-Learning techniques can be further differentiated into three categories

In each of these categories, there is a unique learner, meta-learner, and vectors with labels that match Data-Time-Spatial vectors into a set of networking processes to weigh real-world scenarios labeled with context and inferences.

All the recent Image Processing and Voice Search techniques use the Meta-Learning techniques for their outcomes.

Adversarial ML is one of the fastest-growing and most sophisticated of all ML techniques. It is defined as the ML technique adopted to test and validate the effectiveness of any Machine Learning program in an adverse situation.

As the name suggests, its the antagonistic principle of genuine AI, but used nonetheless to test the veracity of any ML technique when it encounters a unique, adverse situation. It is mostly used to fool an ML model into doubting its own results, thereby leading to a malfunction.

Most ML models are capable of generating answer for one single parameter. But, can it be used to answer for x (unknown or variable) parameter. Thats where the Causal Inference ML techniques comes into play.

Most AI ML courses online are teaching Causal inference as a core ML modeling technique. Causal inference ML technique is defined as the causal reasoning process to draw a unique conclusion based on the impact variables and conditions have on the outcome. This technique is further categorized into Observational ML and Interventional ML, depending on what is driving the Causal Inference algorithm.

Also commercially popularized as Explainable AI (X AI), this technique involves the use of neural networking and interpretation models to make ML structures more easily understood by humans.

Deep Learning Interpretability is defined as the ML specialization to remove black boxes in AI models, providing decision-makers and data officers to understand data modeling structures and legally permit the use of AI ML for general purposes.

The ML technique may use one or more of these techniques for Deep Learning Interpretation.

Any data can be accurately plotted using graphs. In Machine Learning techniques, a graph is a data structure consisting of two components, Vertices (or nodes) and Edges.

Graph ML networks is a specialized ML technique used to connect problems with edges and graphs. Graph Neural Networks (NNs) give rise to the category of Connected NNs (CNSS) and AI NNs (ANN).

There are at least 50 more ML techniques that could be learned and deployed using various NN models and systems. Click here to know of the leading ML companies that are constantly transforming Data Science applications with AI ML techniques.

(To share your insights about ML techniques and commercial applications, please write to us at info@aithority.com)

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10 Machine Learning Techniques and their Definitions - AiThority

Google has an update that might make you hate your alarm a little bit less: a new feature lets it automatically change up what your alarm plays based on the time of day and the weather, theoretically playing something slightly more appropriate than the same awful song you hear day in and out. At least, itll be nice as long as youre okay with waking up to AI-generated piano.

The feature is confined to a single device for now: Lenovos Smart Clock, a small smart display that basically has the functionality of a Google Home Mini paired with a screen that can show the time and weather. Google says this feature which it calls Impromptu is part of Google Assistant, though, which suggests it should reach other smart displays, and perhaps even phones, in the future. The announcement doesnt say when or whether itll expand, however.

Google says all of the music is created and chosen by Magenta, an open-source music tool built around machine learning that Google has been creating. In a blog post, Google says the system might select this song if the weather is below 50 degrees (Im assuming Fahrenheit) and early in the morning. I dont know exactly what about this song says cool and pre-dawn, but Id be down to listen to anything other than the default alarm tone that Ive heard every day for years.

The feature is rolling out globally today to Lenovos device. The smart clock, which used to retail for $80, now appears to be down to $50, making it a lot more competitive with Amazons $60 Echo Show 5.

Excerpt from:

Google is using machine learning to make alarm tones based on the time and weather - The Verge

Researchers have used a machine-learning algorithm to study protein appearance and discover common features that influence function, which could be used to design artificial cells.

Researchers at EPFL have developed a new way to predict a protein's interactions with other proteins and biomolecules and its biochemical activity, merely by observing its surface (credit: Laura Persat / 2019 EPFL).

A new machine learning-driven technique has been able to predict the interactions between proteins and describe biochemical activity based on surface appearance.

The study was conducted at the Laboratory of Protein Design & Immunoengineering (LPDI), Switzerland, in collaboration with other researchers.

According to the team, the method, known as MaSIF, could support the development of protein-based components for artificial cells in novel therapeutics.

Scientists have developed a new way to predict a proteins interactions with other proteins and biomolecules and its biochemical activity, merely by observing its surface (credit: Laura Persat / 2019 EPFL).

The researchers took a vast set of protein surface data and fed the chemical and geometric properties into a machine-learning algorithm, training it to match these properties with particular behaviour patterns and activity. They used the remaining data to test the algorithm.

By scanning the surface of a protein, our method can define a fingerprint, which can then be compared across proteins, says Pablo Gainza, the first author of the study.

The team found that proteins performing similar interactions share common features.

The algorithm can analyse billions of protein surfaces per second, says LPDI director Bruno Correia. Our research has significant implications for artificial protein design, allowing us to program a protein to behave a certain way merely by altering its surface chemical and geometric properties.

The method could also be used to analyse the surface structure of other types of molecules, say the researchers.

The findings were published in Nature Methods.

Read the rest here:

Appearance of proteins used to predict function with machine learning - Drug Target Review

AI algorithms are helping scientists map ten billion cells from the human body in an attempt to unlock the mysteries of how life emerges from the embryo, or how diseases like cancer manifest.

Dana Peer, the current chair and professor in computational and systems biology at the Memorial Sloan Kettering Cancer Center, a research lab focused cancer treatment in New York, described machine learning as a toolbox for building the Human Cell Atlas. The project aims to turn data from billions of tissue sample cells into 3D maps so scientists can visualize our bodies down at the smallest units.

Life develops from a single embryo. How does that initial cell go on to produce nearly 40 trillion cells to form a human body that is able to move and think?

The process can be largely described by cell differentiation, where a stem cell morphs into a specialized unit that carries out a vital function for a particular organ. Genetic information stored in DNA and encoded into every cell carries instructions on how to build different cell types for the body.

Although scientists broadly understand the process, theyre still perplexed at how it works down at the cellular level.

Cells are like tiny computers that get input from their environment, signals [and nutrients] from other cells, Peer said on stage during the Conference on Neural Information Processing Systems in Vancouver on Tuesday.

They have all sorts of proteins that are their processing devices. They make decisions, they interact with each other due to their biochemistry and molecular biology, and decide whether theyre going to proliferate, make more copies of themselves, differentiate, enter a new cell type, activate, or release some molecule to talk with another cell. Theyre really like little computers and we want to know how they work.

The problem with studying cells is, however, the sheer amount of data they produce. The genetic code describing the RNA in cells from one tissue sample is represented as a series of numbers in a giant matrix. At first glance these matrices dont make much sense but they can be turned into 3D maps with the help of AI algorithms.

Common machine learning techniques and models like t-SNE, k-nearest neighbors, Markov chains, or even deep learning have allowed biologists to visualize the behavior of cells. The jumbled stream of numbers describing a cell can now be represented as a clear graph that clusters the data by cell type and function.

Scientists have managed to trace the source of acute lymphoblastic leukemia, the most common cancer in children, to a rare cell type that only crops up in seven out of 10,000 cells. Peer described how data visualization has also allowed researchers to discover how a single mutation in a pancreatic cell can lead to cancer.

The mutation tricks the immune system and it can no longer defend our bodies against cancer. All the knowledge gleaned from these visualizations can help scientists develop new drugs and methods that target diseases like cancer and speed up the process of clinical trials.

Peer hopes that by building the Human Cell Atlas, itll serve as a healthy reference for mapping disease. She called it a candy land playground for biologists. But although machine learning algorithms have already had a huge impact, the techniques are more successful in modelling common patterns in data rather than highlighting any anomalous behaviors.

Our goal is not to predict but understand, and in biology, the outlier is often the most important.

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Scientists are using machine learning algos to draw maps of 10 billion cells from the human body to fight cancer - The Register

The NFL and Amazon announced an expansion of their partnership at their annual AWS re:Invent ... [+] conference in Las Vegas that will use artificial intelligence and machine learning to combat player injuries. (Photo by Michael Zagaris/San Francisco 49ers/Getty Images)

Injury prevention in sports is one of the most important issues facing a number of leagues. This is particularly true in the NFL, due to the brutal nature of that punishing sport, which leaves many players sidelined at some point during the season. A number of startups are utilizing technology to address football injury issues, specifically limiting the incidence of concussions. Now, one of the largest companies in the world is working with the league in these efforts.

A week after partnering with the Seattle Seahawks on its machine learning/artificial intelligence offerings, Amazon announced a partnership Thursday in which the technology giant will use those same tools to combat football injuries. Amazon has been involved with the league, with its Next Gen Stats partnership, and now the two companies will work to advance player health and safety as the sport moves forward after its 100th season this year. Amazons AWS cloud services will use its software to analyze large volumes of player health data the league is already collecting. It will also scan video images with the objective of helping teams treat injuries and rehabilitate players more effectively. The larger goal will be to create a new Digital Athlete platform to anticipate injury before it even takes place.

This partnership expands the quickly growing relationship between the NFL and Amazon/AWS. as the two have already teamed up for two years with the leagues Thursday Night Football games streamed on the companys Amazon Prime Video platform. Amazon paid $130 million for rights that run through next season. The league also uses AWSs ML Solutions Lab,as well as Amazons SageMaker platform, that enables data scientists and developers to build and develop machine learning models that can also lead to the leagues ultimate goal of predicting and limiting player injury.

The NFL is committed to re-imagining the future of football, said NFL Commissioner Roger Goodell. When we apply next-generation technology to advance player health and safety, everyone wins from players to clubs to fans. The outcomes of our collaboration with AWS and what we will learn about the human body and how injuries happen could reach far beyond football. As we look ahead to our next 100 seasons, were proud to partner with AWS in that endeavor.

The new initiative was announced as part of Amazons AWS re:Invent conference in Las Vegas on Thursday. Among the technologies that AWS and the league announced in its Digital Athlete platform is a computer-simulated model of an NFL player that will model infinite scenarios within NFL gameplay in order to identify a game environment that limits the risk to a player. Digital Athlete uses Amazons full arsenal of technologies, including the AI, ML and computer vision technology that is used with Amazons Rekognition tool and that uses enormous data sets encompassing historical and more modern video to identify a wide variety of solutions, including the prediction of player injury.

By leveraging the breadth and depth of AWS services, the NFL is growing its leadership position in driving innovation and improvements in health and player safety, which is good news not only for NFL players but also for athletes everywhere, said Andy Jassy, CEO of AWS. This partnership represents an opportunity for the NFL and AWS to develop new approaches and advanced tools to prevent injury, both in and potentially beyond football.

These announcements come at a time when more NFL players are utilizing their large platforms to bring awareness to injuries and the enormous impact those injuries have on their bodies. Former New England Patriots tight end Rob Gronkowski has been one of the most productive NFL players at his position in league history but he had to retire from the league this year, at the age of 29, due to a rash of injuries.

The future Hall of Fame player estimated that he suffered probably 20 concussions in his football career. These admissions have significant consequences on youth participation rates in the sport. Partnerships like the one announced yesterday will need to be successful in order for the sport to remain on solid footing heading into the new decade.

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The NFL And Amazon Want To Transform Player Health Through Machine Learning - Forbes