Machine learning had a rich history long before deep learning reached fever pitch. Researchers and vendors were using machine learning algorithms to develop a variety of models for improving statistics, recognizing speech, predicting risk and other applications.

While many of the machine learning algorithms developed over the decades are still in use today, deep learning -- a form of machine learning based on multilayered neural networks -- catalyzed a renewed interest in AI and inspired the development of better tools, processes and infrastructure for all types of machine learning.

Here, we trace the significance of deep learning in the evolution of machine learning, as interpreted by people active in the field today.

The story of machine learning starts in 1943 when neurophysiologist Warren McCulloch and mathematician Walter Pitts introduced a mathematical model of a neural network. The field gathered steam in 1956 at a summer conference on the campus of Dartmouth College. There, 10 researchers came together for six weeks to lay the ground for a new field that involved neural networks, automata theory and symbolic reasoning.

The distinguished group, many of whom would go on to make seminal contributions to this new field, gave it the name artificial intelligence to distinguish it from cybernetics, a competing area of research focused on control systems. In some ways these two fields are now starting to converge with the growth of IoT, but that is a topic for another day.

Early neural networks were not particularly useful -- nor deep. Perceptrons, the single-layered neural networks in use then, could only learn linearly separable patterns. Interest in them waned after Marvin Minsky and Seymour Papert published the book Perceptrons in 1969, highlighting the limitations of existing neural network algorithms and causing the emphasis in AI research to shift.

"There was a massive focus on symbolic systems through the '70s, perhaps because of the idea that perceptrons were limited in what they could learn," said Sanmay Das, associate professor of computer science and engineering at Washington University in St. Louis and chair of the Association for Computing Machinery's special interest group on AI.

The 1973 publication of Pattern Classification and Scene Analysis by Richard Duda and Peter Hart introduced other types of machine learning algorithms, reinforcing the shift away from neural nets. A decade later, Machine Learning: An Artificial Intelligence Approach by Ryszard S. Michalski, Jaime G. Carbonell and Tom M. Mitchell further defined machine learning as a domain driven largely by the symbolic approach.

"That catalyzed a whole field of more symbolic approaches to [machine learning] that helped frame the field. This led to many Ph.D. theses, new journals in machine learning, a new academic conference, and even helped to create new laboratories like the NASA Ames AI Research branch, where I was deputy chief in the 1990s," said Monte Zweben, CEO of Splice Machine, a scale-out SQL platform.

In the 1990s, the evolution of machine learning made a turn. Driven by the rise of the internet and increase in the availability of usable data, the field began to shift from a knowledge-driven approach to a data-driven approach, paving the way for the machine learning models that we see today.

The turn toward data-driven machine learning in the 1990s was built on research done by Geoffrey Hinton at the University of Toronto in the mid-1980s. Hinton and his team demonstrated the ability to use backpropagation to build deeper neural networks.

"This was a major breakthrough enabling new kinds of pattern recognition that were previously not feasible with neural nets," Zweben said. This added new layers to the networks and a way to strengthen or weaken connections back across many layers in the network, leading to the term deep learning.

Although possible in a lab setting, deep learning did not immediately find its way into practical applications, and progress stalled.

"Through the '90s and '00s, a joke used to be that 'neural networks are the second-best learning algorithm for any problem,'" Washington University's Das said.

Meanwhile, commercial interest in AI was starting to wane because the hype around developing an AI on par with human intelligence had gotten ahead of results, leading to an AI winter, which lasted through the 1980s. What did gain momentum was a type of machine learning using kernel methods and decision trees that enabled practical commercial applications.

Still, the field of deep learning was not completely in retreat. In addition to the ascendancy of the internet and increase in available data, another factor proved to be an accelerant for neural nets, according to Zweben: namely, distributed computing.

Machine learning requires a lot of compute. In the early days, researchers had to keep their problems small or gain access to expensive supercomputers, Zweben said. The democratization of distributed computing in the early 2000s enabled researchers to run calculations across clusters of relatively low-cost commodity computers.

"Now, it is relatively cheap and easy to experiment with hundreds of models to find the best combination of data features, parameters and algorithms," Zweben said. The industry is pushing this democratization even further with practices and associated tools for machine learning operations that bring DevOps principles to machine learning deployment, he added.

Machine learning is also only as good as the data it is trained on, and if data sets are small, it is harder for the models to infer patterns. As the data created by mobile, social media, IoT and digital customer interactions grew, it provided the training material deep learning techniques needed to mature.

By 2012, deep learning attained star status after Hinton's team won ImageNet, a popular data science challenge, for their work on classifying images using neural networks. Things really accelerated after Google subsequently demonstrated an approach to scaling up deep learning across clusters of distributed computers.

"The last decade has been the decade of neural networks, largely because of the confluence of the data and computational power necessary for good training and the adaptation of algorithms and architectures necessary to make things work," Das said.

Even when deep neural networks are not used directly, they indirectly drove -- and continue to drive -- fundamental changes in the field of machine learning, including the following:

Deep learning's predictive power has inspired data scientists to think about different ways of framing problems that come up in other types of machine learning.

"There are many problems that we didn't think of as prediction problems that people have reformulated as prediction problems -- language, vision, etc. -- and many of the gains in those tasks have been possible because of this reformulation," said Nicholas Mattei, assistant professor of computer science at Tulane University and vice chair of the Association for Computing Machinery's special interest group on AI.

In language processing, for example, a lot of the focus has moved toward predicting what comes next in the text. In computer vision as well, many problems have been reformulated so that, instead of trying to understand geometry, the algorithms are predicting labels of different parts of an image.

The power of big data and deep learning is changing how models are built. Human analysis and insights are being replaced by raw compute power.

"Now, it seems that a lot of the time we have substituted big databases, lots of GPUs, and lots and lots of machine time to replace the deep problem introspection needed to craft features for more classic machine learning methods, such as SVM [support vector machine] and Bayes," Mattei said, referring to the Bayesian networks used for modeling the probabilities between observations and outcomes.

The art of crafting a machine learning problem has been taken over by advanced algorithms and the millions of hours of CPU time baked into pretrained models so data scientists can focus on other projects or spend more time on customizing models.

Deep learning is also helping data scientists solve problems with smaller data sets and to solve problems in cases where the data has not been labeled.

"One of the most relevant developments in recent times has been the improved use of data, whether in the form of self-supervised learning, improved data augmentation, generalization of pretraining tasks or contrastive learning," said Juan Jos Lpez Murphy, AI and big data tech director lead at Globant, an IT consultancy.

These techniques reduce the need for manually tagged and processed data. This is enabling researchers to build large models that can capture complex relationships representing the nature of the data and not just the relationships representing the task at hand. Lpez Murphy is starting to see transfer learning being adopted as a baseline approach, where researchers can start with a pretrained model that only requires a small amount of customization to provide good performance on many common tasks.

There are specific fields where deep learning provides a lot of value, in image, speech and natural language processing, for example, as well as time series forecasting.

"The broader field of machine learning is enhanced by deep learning and its ability to bring context to intelligence. Deep learning also improves [machine learning's] ability to learn nonlinear relationships and manage dimensionality with systems like autoencoders," said Luke Taylor, founder and COO at TrafficGuard, an ad fraud protection service.

For example, deep learning can find more efficient ways to auto encode the raw text of characters and words into vectors representing the similarity and differences of words, which can improve the efficiency of the machine learning algorithms used to process it. Deep learning algorithms that can recognize people in pictures make it easier to use other algorithms that find associations between people.

More recently, there have been significant jumps using deep learning to improve the use of image, text and speech processing through common interfaces. People are accustomed to speaking to virtual assistants on their smartphones and using facial recognition to unlock devices and identify friends in social media.

"This broader adoption creates more data, enables more machine learning refinement and increases the utility of machine learning even further, pushing even further adoption of this tech into people's lives," Taylor said.

Early machine learning research required expensive software licenses. But deep learning pioneers began open sourcing some of the most powerful tools, which has set a precedent for all types of machine learning.

"Earlier, machine learning algorithms were bundled and sold under a licensed tool. But, nowadays, open source libraries are available for any type of AI applications, which makes the learning curve easy," said Sachin Vyas, vice president of data, AI and automation products at LTI, an IT consultancy.

Another factor in democratizing access to machine learning tools has been the rise of Python.

"The wave of open source frameworks for deep learning cemented the prevalence of Python and its data ecosystem for research, development and even production," Globant's Lpez Murphy said.

Many of the different commercial and free options got replaced, integrated or connected to a Python layer for widespread use. As a result, Python has become the de facto lingua franca for machine learning development.

Deep learning has also inspired the open source community to automate and simplify other aspects of the machine learning development lifecycle. "Thanks to things like graphical user interfaces and [automated machine learning], creating working machine learning models is no longer limited to Ph.D. data scientists," Carmen Fontana, IEEE member and cloud and emerging tech practice lead at Centric Consulting, said.

For machine learning to keep evolving, enterprises will need to find a balance between developing better applications and respecting privacy.

Data scientists will need to be more proactive in understanding where their data comes from and the biases that may inadvertently be baked into it, as well as develop algorithms that are transparent and interpretable. They also need to keep pace with new machine learning protocols and the different ways these can be woven together with various data sources to improve applications and decisions.

"Machine learning provides more innovative applications for end users, but unless we're choosing the right data sets and advancing deep learning protocols, machine learning will never make the transition from computing a few results to providing actual intelligence," said Justin Richie, director of data science at Nerdery, an IT consultancy.

"It will be interesting to see how this plays out in different industries and if this progress will continue even as data privacy becomes more stringent," Richie said.

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Deep learning's role in the evolution of machine learning - TechTarget

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Supply chain management has always been one of the most complicated business processes. Theres the complexity of multiple systems internal to a corporation, including accounting, manufacturing, inventory and more. Then theres the need to share information up and down the chain. It was a problem before computers, in the mainframe days, and still exists as a challenge today. Machine learning (ML) has begun to have an impact on the supply chain, and its overdue.

Let me begin by pointing out that I am talking about an ML definition that isnt limited to artificial intelligence. Theres certainly AI in areas such as natural language and some predictive arenas, but the inclusion of complex statistical analysis provided by procedural algorithms also provide insights that mean inclusion in ML.

In order to understand the broad opportunity for ML in the supply chain, we need to look separately at the two areas mentioned above.

Back in the 1980s, I worked on a manufacturing companys inventory system. The folks who built it only talked with accounting. It was great for accountants, but the user interface and data available were almost useless to the inventory people and that was in a single system. The problem has only become exponentially complex.

Data silos. IT professionals have been complaining about data silos for decades, and theyre still a problem. The goal of a consistent, complete, corporate view of data is still just that, a goal. ERP, CRM, and other systems still have multiple, redundant data items with different data types. Manufacturing data doesnt fit accounting data which doesnt match sales order systems.

In some ways, data moves more slowly than physical products, says Rob Bailey, CEO, BackboneAI. Breaking down the walls between data sources, and then aligning data to present a clear and accurate understanding of the supply chain is something that machine learning is addressing.

Lets use one example that is a necessary bane of product existence, the stock keeping unit (SKU). The SKU is a code to identify a specific type of product, and every company creates to track inventory. Note that every company, which well discuss in the next section. For the purposes of an organization, the growth of departments, divisions, and national branches, even a single product can have different SKUs in the multiple systems within an organization.

Mediating between multiple systems can include tens of thousands of SKUs, so identifying similar products is something that can go much faster with ML. Natural language processing (NLP) is useful for the rapid scanning of product descriptions and then probabilistic categorization can link separate SKUs to provide an overall picture of a single product. This can speed the creation of corporate metadata and the ability to provide a global picture of products.

As complex as the issue of product knowledge is within even a medium size organization, companies of all sizes are challenged by the sharing of data across the supply chain. One key segment of the chain is that between suppliers and distributors. This is often a many-to-many relationship and both sides want insight into the other. The distributors can struggle to keep their product details clear for all the suppliers, with major issues about catalog quality and timeliness. At the same time, suppliers want an accurate assessment of how their products are doing with each distributor, what the turnover is and if the distributors media accurately represents the products.

Companies such as Backbone AI are workingto help both sides. One of the initial struggles they see is the eternal one of IT: lack of resources. The IT organizations are so busy working to handle internal requests that external needs are often given lower priority. Machine learning can help both distributors and suppliers become more efficient, said Mr. Bailey. Analysis of public information can leverage the 80/20 rule, eliminating the large percentage of basic work and letting them focus, much faster, on the data that is left.

That web crawling is even more reliant on NLP than the aforementioned mediation between databases of SKUs. In addition, similar routines to categorize product information can be speed up with both statistical approaches and neural networks. That provides increased accuracy with a faster turnaround, helping increase the responsiveness of modern supply chains working to run in near real-time.

By automating large segments of data analysis and flow, ML, even when it is not doing anything novel, can provide efficiencies to corporations.

While ML is being applied to better tracking of products and information in the supply chain, theres one interesting area of growth. IoT is spreading, and on area its already connecting is in the trucking industry. More and more information is being captured during transportation. Rob Bailey mentioned something that could significantly help in the food industry.

We all know of many recalls of food product recalled because of problems such as e coli. However, its not only at the original where food can be spoiled. Refrigerated trucks and train cars are at the core of the modern food industry. Advances in IoT in those containers can provide information that tracks location of SKUs within a container, which can be combined with temperature and other data, then analyzed with ML, to help locate specific risks without creating unneeded food waste.

While the current focus is, naturally, on the difficult work on normalizing information within and across organizations, the next step, as IoT and connectivity increase in capabilities, is to stretch to eventually track products from raw material, through manufacturing, to the consumer. The supply chain is complex, and machine learning will be a critical tool in the continuing improvement of supply chain management.

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Unifying The Supply Chain With Machine Learning Organized Information, Increases Speed, And Improves Efficiency - Forbes

Its been a few years since Ilast wroteabout the idea of using synthetic data to train machine learning models.After having three recent discussions on the topic, I figured its time to revisit the technology, especially as it seems to be gaining ground in mainstream adoption.

Back in 2018, at Microsoft Build, I saw a demonstration of a drone flying over a pipeline as it inspected it for leaks or other damage. Notably, the drones visual inspection model was trained using both actual data and simulated data. Use of the synthetic data helped teach the machine learning model about outliers and novel conditions it wasnt able to encounter using traditional training. Italso allowed Microsoft researchers to train the model more quickly and without the need to embark on as many expensive, data-gathering flights as it would have had to otherwise.

The technology is finally starting to gain ground. In April, a startup calledAnyverse raised 3million ($3.37 million)for its synthetic sensor data,while another startup,AI.Reverie,published a paper about how it used simulated data to train a model to identify planes on airport runways.

After writing that initial story, I heard very little about synthetic data untilmy conversation earlier this month with Dan Jeavons, chief data scientist at Shell. When I asked him about Shells machine learning projects, using simulated data was one that he was incredibly excited about because it helps build models that can detect problems that occur only rarely.

I think its a really interesting way to get info on the edge cases that were trying to solve, he said. Even though we have a lot of data, the big problem that we have is that, actually, we often only had a very few examples of what were looking for.

In the oil business, corrosion in factories and pipelines is a big challenge, and one that can lead to catastrophic failures. Thats why companies are careful about not letting anything corrode to the point where it poses a risk. But that also means the machine learning models cant be trained on real-world examples of corrosion. So Shell uses synthetic data to help.

As Jeavons explained, Shell is also using synthetic data to try and solve the problem of people smoking at gas stations. Shelldoesnthave a lot of examples because the cameras dont always catch the smokers; in other cases, theyre too far away or arent facing the camera. So the company is working hard on combining simulated synthetic data with real data to build computer vision models.

Almost always the things were interested in are the edge cases rather than the general norm, said Jeavons. And its quite easy to detect the edge [deviating] from the standard pattern, but its quite hard to detect the specific thing that you want.

In the meantime, startup AI.Reverie endeavored to learn more about the accuracy of synthetic data. The paper it published, RarePlanes: Synthetic Data Takes Flight, lays out how its researchers combined satellite imagery of planes parked at airports that was annotated and validated by humans with synthetic data created by machine.

When using just synthetic data, the model was only about 55% percent accurate, whereas when it only used real-world data that number jumped to 73%. But by makingreal-world data 10% of the training sample and using synthetic data for the rest, the models accuracy came in at 69%.

Paul Walborsky, the CEO of AI.Reverie (and the former CEO at GigaOM; in other words, my former boss), says that synthetic datais going to be a big business. Companies using such data need to account for ways that their fake data can skew the model, but if they can do that, they can achieve robust models faster and at a lower cost than if they relied on real-world data.

So even though IoT sensors are throwing off petabytes of data, it would be impossible to annotate all of it and use it for training models. And as Jeavons points out, those petabytes of data may not have the situation you actually want the computer to look for. In other words, expect the wave of synthetic and simulated data to keep on coming.

Were convinced that, actually, this is going to be the future in terms of making things work well, said Jeavons, both in the cloud and at the edge for some of these complex use cases.

Related

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Fake data is great data when it comes to machine learning - Stacey on IoT

Data is everywhere growing across variety and velocity in both structured and unstructured formats. Leveraging this chaotic data generated at ever-increasing speeds is often a mammoth task. Even powerful AI and machine learning capabilities lose their accuracy if they dont have the right data to support them. The rise in data complexity, makes it challenging for IT operations to get the best from Artificial Intelligence and ML algorithms for digital transformation.

The secret lies in acknowledging this data, to use its explosion as an opportunity to drive intelligence, automation, effectiveness and productivity with Artificial intelligence for IT operations (AIOps). In simple words, AIOps refers to the automation of IT operations artificial intelligence (AI), freeing enterprise IT operations by inputs of operational data to achieve the ultimate data automation goals.

AIOps of any enterprise stands firmly on four pillars, collectively referred to as the key dimensions of IT operations monitoring:

Data Selection & Filtering

Modern IT environments create noisy IT data, collating this data and filtering for Excel, AI and ML models is a tedious task. Taking massive amounts of redundant data selecting data elements of interest often means filtering out up to 99% of data.

Discovering Data Patterns

Unearthing data patterns implies to collate filtered data to establish meaningful relationships between the selected data groups for further analysis.

Data Collaboration

Data analysis fosters collaboration among interdisciplinary teams across global enterprises, besides preserving valuable data intelligence that can accelerate future synergies within the enterprise.

Solution Automation

This dimension relates to automating data responses and remediation, in a bid to more precise solutions achieved at a quicker TAT.

A responsible AIOps platform combines AI, machine learning and big data with a mature understanding of IT operations. It makes way to assimilate real-time and historical data from any source for cutting edge AI and ML capabilities. This makes it possible for enterprises to get a hold of problems before they even happen by leveraging on clustering, anomaly detection, prediction, statistical thresholding, predictive analytics, forecasting, and more.

IT environments have broken silos and currently exceeding the realms of the manual human scale of operations. Traditional approaches to managing IT find redundancy over the dynamic environments governed by technology.

1. Data pipelines that ITOps need to retain is exponentially increasing encompassing a larger number of events and alerts. With the introduction of APIs, digital or machine users, mobile applications, and IoT devices, modern enterprises receive higher service ticket volumes. A trend that is becoming too complex for manual reporting and analysis.

2. As organizations walk on the digital transformation path, seamless ITOps becomes indispensable. The accessibility of technology has changed user expectations across industries and vertices. This calls for an immediate reaction to IT events especially when an issue impacts user experience.

3. The introduction of edge computing and cloud infrastructure empowers the line of business (LOB) functions to build and host their own IT solutions and applications over the cloud to be accessed anytime anywhere. This calls for an increase in budgetary allocation increase and more computing power (that can be leveraged) to be added from outside core IT.

AIOps bridges the gap between service management, performance management, and automation within the IT eco-system to accomplish the continuous goal of IT operation improvements. AIOps creates a game plan that delivers within the new accelerated IT environments, to identify patterns in monitoring, service desk, capacity addition and data automation across hybrid on-premises and multi-cloud environments.

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Kamalika Some is an NCFM level 1 certified professional with previous professional stints at Axis Bank and ICICI Bank. An MBA (Finance) and PGP Analytics by Education, Kamalika is passionate to write about Analytics driving technological change.

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How Does AIOps Integrate AI and Machine Learning into IT Operations? - Analytics Insight

Prophecy Market Insights recently presented Machine Learning in Medical Imaging market report which provides reliable and sincere insights related to the various segments and sub-segments of the market. The market study throws light on the various factors that are projected to impact the overall dynamics of the Machine Learning in Medical Imaging market over the forecast period (2019-2029).

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Machine Learning in Medical Imaging Market Strategies and Insight Driven Transformation 2020-2030 - Cole of Duty

QuantHouse, the global provider of end-to-end systematic trading solutions including innovative market data services, algo trading platform and infrastructure products and part of Iress (IRE.ASX), today announced that Trading System Lab(TSL) has added their machine learning capabilities as part of the QuantFactory cloud backtesting suite.

The QuantFactory cloud backtesting suite provides a fully configurable environment in which clients can develop, backtest, optimise and implement quantitative trading strategies that can later be executed in a standalone, live-trading environment. Machine learning outputs from TSL are integrated into the QuantDeveloper module of QuantFactory.

Machine learning delivers a number of advantages to clients which includes increasing the scope of trading strategies available, increasing the number of markets an individual can monitor and respond to and, incorporating a wider range of data sources.

TSL provides machine learning capabilities that automatethe design and development of trading strategies. This enables TSL to deliver far more innovative strategies, design thousands of strategies per second and per instance, reduces time to market and is interoperable with all data, markets, frequency and programming languages.

Salloum Abousaleh, Managing Director - Americas, QuantHouse,said, Machine learning increases the scope of trading strategies available and the number of markets and data sources that an individual can process and respond to. QuantFactory and TSL combined, drastically reduce the timeto engineer and deployalgorithmic trading strategies and automatize what is often a tedious manual process. This collaboration is part of our ongoing commitment to simplify access to quantitative trading that enables our clients to reduce cost, improve quality, decrease time to market and expand their universe of novel strategies through Machine Learning.

Mike Barna, CEO, Trading System Lab, added, We are delighted to deliver our machine learning capabilities to the global QuantHouse community. Our integration with QuantFactory allows QuantHouse clients to rapidly deploy new strategies without writing a single line of code, while leveraging QuantHouse's leading research and backtesting environment helps optimize and deploy the trading models generated by our platform.

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QuantHouse To Provide TSL Machine Learning Capabilities As Part Of The QuantFactory Cloud Backtesting Suite - Offering Full-Automation And...

Smartphones today have become so powerful that sometimes even mid-range handsets can support some fancy machine learning and AI applications. Most of those, however, still rely on cloud-hosted neural networks, machine learning models, and processing, which has both privacy and efficiency drawbacks. Contrary to what most would expect, Google has been moving to offload much of that machine learning activity from the cloud to the device and its latest machine learning development tool is its latest step in that direction.

Googles machine learning or ML Kit SDK has been around for two years now but it has largely been tied to its Firebase mobile and web development platform. Like many Google products, this creates a dependency on a cloud-platform that entails not just some latency due to network bandwidth but also risks leaking potentially private data in transit.

While Google is still leaving that ML Kit + Firebase combo available, it is now also launching a standalone software development kit or SDK for both Android and iOS app developers that focuses on on-device machine learning. Since everything happens locally, the users privacy is protected and the app can function almost in real-time regardless of the speed of the Internet connection. In fact, an ML-using app can even work offline for that matter.

The implications of this new SDK can be quite significant but it still depends on developers switching from the Firebase version to the standalone SDK. To give them a hand, Google created a code lab that combines the new ML Kit with its CameraX app in order to translate text in real-time without connecting to the Internet.

This can definitely help boost confidence in AI-based apps if the user no longer has to worry about privacy or network problems. Of course, Google would probably prefer that developers keep using the Firebase connection which it even describes as getting the best of both products.

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Googles new ML Kit SDK keeps all machine learning on the device - SlashGear

Google Executive Chairman Eric Schmidt

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Schmidt Futures, the philanthropic foundation set up by billionaires Eric and Wendy Schmidt, is funding a new program at the University of Cambridge that's designed to equip young researchers with machine learning and artificial intelligence skills that have the potential to accelerate their research.

The initiative known as the Accelerate Program for Scientific Discovery will initially be aimed at researchers in science, technology, engineering, mathematics and medicine. However, it will eventually be available for those studying arts, humanities and social science.

Some 32 PhD students will receive machine-learning training through the program in the first year, the university said, adding that the number will rise to 160 over five years. The aim is to build a network of machine-learning experts across the university.

"Machine learning and AI are increasingly part of our day-to-day lives, but they aren't being used as effectively as they could be, due in part to major gaps of understanding between different research disciplines," Professor Neil Lawrence, a former Amazon director who will lead the program, said in a statement.

"This program will help us to close these gaps by training physicists, biologists, chemists and other scientists in the latest machine learning techniques, giving them the skills they need."

The scheme will be run by four new early-career specialists, who are in the process of being recruited.

The Schmidt Futures donation will be used partly to pay the salaries of this team, which will work with the university's Department of Computer Science and Technology and external companies.

Guest lectures will be provided by research scientists at DeepMind, the London-headquartered AI research lab that was acquired by Google.

The size of the donation from Schmidt Futures has not been disclosed.

"We are delighted to support this far-reaching program at Cambridge," said Stuart Feldman, chief scientist at Schmidt Futures, in a statement. "We expect it to accelerate the use of new techniques across the broad range of research as well as enhance the AI knowledge of a large number of early-stage researchers at this superb university."

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Eric and Wendy Schmidt back Cambridge University effort to equip researchers with A.I. skills - CNBC