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The performance of deep neural networks (DNNs) relies heavily on their structures, and designing a good structure (aka architecture) tends to require extensive effort from human experts. The idea of an automatic structure-learning algorithm that can achieve performance on par with the best human-designed structures is thus increasingly appealing to machine learning researchers.

In the paper Learning Structures for Deep Neural Networks, a team from OneFlow and Microsoft explores unsupervised structure learning, leveraging the efficient coding principle, information theory and computational neuroscience to design a structure learning method that does not require labelled information and demonstrates empirically that larger entropy outputs in a deep neural network lead to better performance.

The researchers start with the assumption that the optimal structure of neural networks can be derived from the input features without labels. Their study probes whether it is possible to learn good DNN network structures from scratch in a fully automatic fashion, and what would be a principled way to reach this end.

The team references a principle borrowed from the biological nervous system domain the efficient coding principle which posits that a good brain structure forms an efficient internal representation of external environments. They apply the efficient coding principle to DNN architecture, proposing that the structure of a well-designed network should match the statistical structure of its input signals.

The efficient coding principle suggests that the mutual information between a models inputs and outputs should be maximized, and the team presents a solid Bayesian optimal classification theoretical foundation to support this. Specifically, they show that the top layer of any neural network (softmax linear classifier) and the independency between the nodes in the top hidden layer constitute a sufficient condition for making the softmax linear classifier act as a Bayesian optimal classifier. This theoretical foundation not only backs up the efficient coding principle, it also provides a way to determine the depth of a DNN.

The team then investigates how to leverage the efficient coding principle in the design of a structure-learning algorithm, and shows that sparse coding can implement the principle under the assumption of zero-peaked and heavy-tailed prior distributions. This suggests that an effective structure learning algorithm can be designed based on global group sparse coding.

The proposed structure-learning with sparse coding algorithm learns a structure layer by layer in a bottom-up manner. The raw features are at layer one, and given the predefined number of nodes in layer two, the algorithm will learn the connection between these two layers, and so on.

The researchers also describe how this proposed algorithm can learn inter-layer connections, handle invariance, and determine DNN depth. Finally, they conduct intensive experiments on the popular CIFAR-10 data set to evaluate the classification accuracies of their proposed structure learning method, the role of inter-layer connections, and the role of structure masks and network depth.

The results show that a learned-structure single-layer network achieves an accuracy of 63.0 percent, outperforming the single-layer baseline of 60.4 percent. In an inter-layer connection density evaluation experiment, the structures generated by the sparse coding approach outperform random structures, and at the same density level, always outperform the sparsifying-restricted Boltzmann machines (RBM) baseline. In the teams structure mask role evaluation, the structure prior provided by sparse coding is seen to improve performance. The network depth experiment meanwhile empirically justifies the proposed approach for determining DNN depth via coding efficiency.

Overall, the research proves the efficient coding principles effectiveness for unsupervised structure learning, and that the proposed global sparse coding-based structure-learning algorithms can achieve performance comparable with the best human-designed structures.

The paper Learning Structures for Deep Neural Networks is on arXiv.

Author: Hecate He |Editor: Michael Sarazen, Chain Zhang

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Microsoft & OneFlow Leverage the Efficient Coding Principle to Design Unsupervised DNN Structure-Learning That Outperforms Human-Designed...

Artificial intelligence (AI) is ushering a new era of technological innovation, paving the way for increased adoption across several industries ranging from healthcare to e-commerce. The novel capabilities offered by AI are empowering businesses to automate the different operations, making them faster and smarter, and enhancing the overall productivity. One of the most significant benefits facilitated by AI is the management of massive datasets, which enables organizations to handle the flow of information efficiently. Overall, AI is making significant headway in the business world, generating robust benefits for integrators and adopters.

Bringing Transparency

Today, firms across the world are incorporating AI-based systems to automate their business processes and enable their workforce to focus on more valuable tasks. However, the incorporation of AI technology is impeded by several challenges, and the biggest one is the lack of transparency. AI systems are often considered as a black box, which makes it challenging to pinpoint logical errors in the underlying algorithms. These challenges in data privacy, protection, and cybersecurity have introduced nuances into the field, making it imperative to develop explainable AI, which offers greater visibility and transparency

Balancing Stability with Innovation

To make the most of AI, enterprises should also focus on incorporating the right tools, talent, and culture. While a myriad of different AI solutions has permeated the marketplace today, businesses still lack the expertise to identify which solution aligns with their organizational goals. This is where having the right partner to help you through every stage of the integration process makes all the difference. These partnerships should be compatible with the values, goals, and strategies of an organization. It is advisable to consider the risk factors and conduct an impact analysis on how the partnership will help drive business growth. For instance, I often collaborate with numerous partners, many of which possess robust and proven solutions. We also work with nascent companies that have a more novel approach to balance the risk versus the impact of the AI integrations.

When choosing partners, businesses should have a firm knowledge of the different areas of improvement within their organization. Successful collaboration relies on minimizing the risks and maximizing the benefits. By narrowing the AI integration to specific processes that require immediate upgrading, the overall workflow can be streamlined seamlessly. Once the relevant areas have been identified, the tech teams can decide on collaborations across the organizational line. Often, enterprises fail to see beyond the hype of AI products and rush into piloting several things without a clear vision of the end result. In such cases, businesses cannot derive the expected value from the solutions. Hence, it is crucial to have a specific goal in mind when integrating AI technology.

Augmenting AI with Robust Leadership

Along with having an AI-first mindset, businesses should be swift and versatile when executing on the AI technology. As a practitioner in this area for several years, I have witnessed a steady rise of interest in data science. One does not need a doctorate to be a data scientist. Perfection comes with practice. My advice to budding professionals is to follow their passion as they have a myriad of resources at their disposal, including journals, and coding courses. As for moving up the leadership ladder, practitioners must take bold decisions and get involved in the community. It also pays to take part in cooperative projects and contribute to the academic community by publishing papers, attending conferences, giving talks, and supporting the cause. Hence, as a leader, it is vital to adapt and change according to the trends, while also focusing on the key areas that need improvement.

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Explainable AI And The Future Of Machine Learning - CIO Applications

Sixty-five years ago, Arthur Samuel went on TV to show the world how the IBM 701 plays checkers. He was interviewed on a live morning news program, sitting remotely at the 701, with Will Rogers Jr. at the TV studio, together with a checkers expert who played with the computer for about an hour. Three years later, in 1959, Samuel published Some Studies in Machine Learning Using the Game of Checkers, in the IBM Journal of Research and Development, coining the term machine learning. He defined it as the programming of a digital computer to behave in a way which, if done by human beings or animals, would be described as involving the process of learning.

On February 24, 1956, Arthur Samuels Checkers program, which was developed for play on the IBM 701, ... [+] was demonstrated to the public on television

A few months after Samuels TV appearance, ten computer scientists convened in Dartmouth, NH, for the first-ever workshop on artificial intelligence, defined a year earlier by John McCarthy in the proposal for the workshop as making a machine behave in ways that would be called intelligent if a human were so behaving.

In some circles of the emerging discipline of computer science, there was no doubt about the human-like nature of the machines they were creating. Already in 1949, computer pioneer Edmund Berkeley wrote inGiant Brains or Machines that Think: Recently there have been a good deal of news about strange giant machines that can handle information with vast speed and skill... These machines are similar to what a brain would be if it were made of hardware and wire instead of flesh and nerves A machine can handle information; it can calculate, conclude, and choose; it can perform reasonable operations with information. A machine, therefore, can think.

Maurice Wilkes, a prominent developer of one of those giant brains, retorted in 1953: Berkeley's definition of what is meant by a thinking machine appears to be so wide as to miss the essential point of interest in the question, Can machines think? Wilkes attributed this not-very-good human thinking to a desire to believe that a machine can be something more than a machine. In the same issue of the Proceeding of the I.R.E that included Wilkes article, Samuel published Computing Bit by Bit or Digital Computers Made Easy. Reacting to what he called the fuzzy sensationalism of the popular press regarding the ability of existing digital computers to think, he wrote: The digital computer can and does relieve man of much of the burdensome detail of numerical calculations and of related logical operations, but perhaps it is more a matter of definition than fact as to whether this constitutes thinking.

Samuels polite but clear position led Marvin Minsky in 1961 to single him out, according to Eric Weiss, as one of the few leaders in the field of artificial intelligence who believed computers could not think and probably never would. Indeed, he pursued his life-long hobby of developing checkers-playing computer programs and professional interest in machine learning not out of a desire to play God but because of the specific trajectory and coincidences of his career. After working for 18 years at Bell Telephone Laboratories and becoming an internationally recognized authority on microwave tubes, he decided at age 45 to move on, as he was certain, says Weiss in his review of Samuels life and work, that vacuum tubes soon will be replaced by something else.

The University of Illinois came calling, asking him to revitalize their EE graduate research program. In 1948, the project to build the Universitys first computer was running out of money. Samuel thought (as he recalled in an unpublished autobiography cited by Weiss) that it ought to be dead easy to program a computer to play checkers and that if their program could beat a checkers world champion, the attention it would generate will also generate the required funds.

The next year, Samuel started his 17-year tenure with IBM, working as a senior engineer on the team developing the IBM 701, IBMs first mass-produced scientific computer. The chief architect of the entire IBM 700 series was Nathaniel Rochester, later one of the participants in the Dartmouth AI workshop. Rochester was trying to decide the word length and order structure of the IBM 701 and Samuel decided to rewrite his checkers-playing program using the order structure that Rochester was proposing. In his autobiography, Samuel recalled that I was a bit fearful that everyone in IBM would consider checker-playing program too trivial a matter, so I decided that I would concentrate on the learning aspects of the program. Thus, more or less by accident, I became one of the first people to do any serious programing for the IBM 701 and certainly one of the very first to work in the general field later to become known as artificial intelligence. In fact, I became so intrigued with this general problem of writing a program that would appear to exhibit intelligence that it was to occupy my thoughts almost every free moment during the entire duration of my employment by IBM and indeed for some years beyond.

But in the early days of computing, IBM did not want to fan the popular fears that man was losing out to machines, so the company did not talk about artificial intelligence publicly, observed Samuel later. Salesmen were not supposed to scare customers with speculation about future computer accomplishments. So IBM, among other activities aimed at dispelling the notion that computers were smarter than humans, sponsored the movie Desk Set, featuring a methods engineer (Spencer Tracy) who installs the fictional and ominous-looking electronic brain EMERAC, and a corporate librarian (Katharine Hepburn) telling her anxious colleagues in the research department: They cant build a machine to do our jobthere are too many cross-references in this place. By the end of the movie, she wins both a match with the computer and the engineers heart.

In his1959 paper, Samuel described his approach to machine learning as particularly suited for very specific tasks, in distinction to the Neural-Net approach, which he thought could lead to the development of general-purpose learning machines. Samuels program searched the computers memory to find examples of checkerboard positions and selected the moves that were previously successful. The computer plays by looking ahead a few moves and by evaluating the resulting board positions much as a human player might do, wrote Samuel.

His approach to machine learning still would work pretty well as a description of whats known as reinforcement learning, one of the basket of machine-learning techniques that has revitalized the field of artificial intelligence in recent years, wrote Alexis Madrigal in a 2017 survey of checkers-playing computer programs. One of the men who wrote the bookReinforcement Learning, Rich Sutton, called Samuels research the earliest work thats now viewed as directly relevant to the current AI enterprise.

The current AI enterprise is skewed more in favor of artificial neural networks (or deep learning) then reinforcement learning, although Googles DeepMind famously combined the two approaches in its Go-playing program which successfully beat Go master Lee Sedol in a five-game match in 2016.

Already popular among computer scientists in Samuels time (in 1951, Marvin Minsky and Dean Edmunds built SNARCStochastic Neural Analog Reinforcement Calculatorthe first artificial neural network, using 3000 vacuum tubes to simulate a network of 40 neurons), the neural networks approach was inspired by a1943 paperby Warren S. McCulloch and Walter Pitts in which they described networks of idealized and simplified artificial neurons and how they might perform simple logical functions, leading to the popular (and very misleading) description of todays artificial neural networks-based AI as mimicking the brain.

Over the years, the popularity of neural networks have gone up and down a number of hype cycles, starting with thePerceptron, a 2-layer artificial neural network that was considered by the U.S. Navy, according to a 1958 New York Times report, to be "the embryo of an electronic computer that.. will be able to walk, talk, see, write, reproduce itself and be conscious of its existence." In addition to failing to meet these lofty expectations, neural networks suffered from a fierce competition from a growing cohort of computer scientists (including Minsky) who preferred the manipulation of symbols rather than computational statistics as the better path to creating a human-like machine.

Inflated expectations meeting the trough of disillusionment, no matter what approach was taken, resulted in at least two periods of gloomy AI Winter. But with the invention and successful application of backpropagation as a way to overcome the limitations of simple neural networks, sophisticated statistical analysis was againon the ascendance, now cleverly labeled as deep learning. In 1988, R. Colin Johnson and Chappell Brown published Cognizers: Neural Networks and Machine That Think, proclaiming that neural networks can actually learn to recognize objects and understand speech just like the human brain and, best of all, they wont need the rules, programming, or high-priced knowledge-engineering services that conventional artificial intelligence systems requireCognizers could very well revolutionize our society and will inevitably lead to a new understanding of our own cognition.

Johnson and Brown predicted that as early as the next two years, neural networks will be the tool of choice for analyzing the contents of a large database. This predictionand no doubt similar ones in the popular press and professional journalsmust have sounded the alarm among those who did this type of analysis for a living in academia and in large corporations, having no clue of what the computer scientists were talking about.

InNeural Networks and Statistical Models, Warren Sarle explained in 1994 to his worried and confused fellow statisticians that the ominous-sounding artificial neural networks are nothing more than nonlinear regression and discriminant models that can be implemented with standard statistical software like many statistical methods, [artificial neural networks] are capable of processing vast amounts of data and making predictions that are sometimes surprisingly accurate; this does not make them intelligent in the usual sense of the word. Artificial neural networks learn in much the same way that many statistical algorithms do estimation, but usually much more slowly than statistical algorithms. If artificial neural networks are intelligent, then many statistical methods must also be considered intelligent.

Sarle provided his colleagues with a handy dictionary translating the terms used by neural engineers to the language of statisticians (e.g., features are variables). In anticipation of todays data science (a more recent assault led by computer programmers) and predictions of algorithms replacing statisticians (and even scientists), Sarle reassured his fellow statisticians that no black box can substitute for human intelligence: Neural engineers want their networks to be black boxes requiring no human interventiondata in, predictions out. The marketing hype claims that neural networks can be used with no experience and automatically learn whatever is required; this, of course, is nonsense. Doing a simple linear regression requires a nontrivial amount of statistical expertise.

In a footnote to his mention of neural networks in his 1959 paper, Samuel cited Warren S. McCulloch who has compared the digital computer to the nervous system of a flatworm, and declared: To extend this comparison to the situation under discussion would be unfair to the worm since its nervous system is actually quite highly organized as compared to [the most advanced artificial neural networks of the day]. In 2019, Facebooks top AI researcher and Turing Award-winner Yann LeCun declared that Our best AI systems have less common sense than a house cat. In the sixty years since Samuel first published his seminal machine learning work, artificial intelligence has advanced from being not as smart as a flatworm to having less common sense than a house cat.

Continued here:
On Thinking Machines, Machine Learning, And How AI Took Over Statistics - Forbes

Elevate your enterprise data technology and strategy at Transform 2021.

This post was written by Vatsal Ghiya, co-founder and chief operating officer of Shaip.

It is hard to imagine fighting a global pandemic without technologies such as Artificial Intelligence (AI) and Machine Learning (ML). The exponential rise of Covid-19 cases around the world left many health infrastructures paralyzed. However, institutions, governments, and organizations were able to fight back with the help of advanced technologies. Artificial intelligence and machine learning, once seen as a luxury for elevated lifestyles and productivity, have become life-saving agents in combating Covid thanks to their innumerable applications.

With allied technologies like Big Data, IoT, and data science, AI offered tools to frontline caregivers and resources to researchers and drug developers. In this post, we explore how AI and ML have helped battle Covid-19 and how they will continue to assist us in recovering from the chaos.

One of the most practical solutions to curb the spread of the virus is through contact tracing. This allows officials and healthcare providers to identify possible victims and carriers they have come in contact with. With this information, they can isolate Covid-positive patients and deliver healthcare solutions.

By coming up with models like SIR (Susceptible, Infectious, and Recovered), caregivers have been able to seamlessly trace contacts, identify vulnerable regions and clusters, announce containment zones, deploy additional healthcare facilities, and more.

In addition to offering prescriptive solutions, AI has also been used to predict positivity and mortality rates, probable mutations of viruses and their reflections on symptoms, and even arrive at dates and times when the contagion will be at its peak. With data-driven statistics and credible AI modules, officials have been able to proactively take measures like announcing lockdowns and shelter in place protocols, procuring vaccines, oxygen cylinders, PPE kits, testing apparatus, and more. This has been of immense help in developing nations with higher population density to stop the spread of the virus, or at least curb the intensity.

The circulation of fake news concerning the virus has been a significant challenge.With social media devoid of supervision or any form of moderation, many people (anonymously) took to social media platforms and instant messengers to circulate false information and conspiracy theories.

From posts that claimed how to cure Covid through home remedies to theories about last Junes Great Reset meeting of the World Economic Forum, thousands of unfounded messages and posts have been going viral. This has been increasing anxiety levels and paranoia among a world population that has already faced a high level of stress. However, through moderations and screening, AI has been doing an incredible job at preventing conspiracy theories and fake information from making the rounds.

Healthcare centers and institutions have been overburdened like never before. For more than a year, many frontline workers including doctors, nurses, and paramedics have been overworked beyond their capacity. With every incoming patient requiring immediate attention, it becomes nearly impossible to maintain sufficient focus to treat everyone.

Thankfully, AI systems have come to the rescue with precise diagnostic chatbots. Through tech concepts such as Natural Language Processing (NLP), an organization called Paginemediche rolled out a chatbot that offered a highly accurate diagnosis of Covid-19 through data fed to it by users.

Based on responses to questions, the chatbot retrieved and offered guidelines, diagnosis, and solutions from the most credible resources and suggested if a patient needed to be isolated, seek medical attention. or understand that their infection is a common flu and not Covid-19. This has slowed the flow of patients to hospitals and healthcare centers to a significant extent.

Vaccines typically are developed through extensive, time-consuming rounds of clinical trials. However, with AI and ML, Covid vaccine development moved forward at lightning speeds compared to previous viral outbreaks. Through pattern recognition and simulation, researchers have been able to come up with the most effective formulas of medications to help the body develop antigens and build immunity against the virus.

Before the AI models were able to provide accurate results for combating Covid, they went through extensive testing. Covid datasets from multiple resources have all assisted solution providers and development companies to launch reliable Covid-related services. For a healthcare-based AI solution to be precise, healthcare datasets that are fed to it should be airtight.

Also, despite offering such revolutionary apps and solutions, AI models for battling Covd are not universally applicable. Every region of the world is fighting its own version of a mutated virus and a population behavior and immune system specific to that particular geographic location. Thats why there is an inherent need for more AI-driven healthcare solutions to penetrate deeper levels of specific world populations.

Any AI or MLcompany looking to develop a solution and contribute to the fight against the virus should be working with highly accurate medical datasets to ensure optimized results. This is the only they you can offer meaningful services or solutions to society right now. The functionality of your solution is crucial. Thats why we recommend you source your healthcare datasets from the most credible avenues in the market, so you have a fully functional solution to roll out and help those in need.

As co-founder and chief operating officer of Shaip, Vatsal Ghiya has 20-plus years of experience in healthcare software and services. Ghiya also co-founded ezDI, a cloud-based software solution company that provides a Natural Language Processing (NLP) engine and a medical knowledge base with products including ezCAC and ezCDI.

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How AI and machine learning help fight the COVID-19 battle - VentureBeat

As enterprise deployments of machine learning continue at a strong pace, including in mission-critical environments such as in contact centers, for fraud detection and in regulated sectors like healthcare and finance for example, they are doing so against a backdrop of rising and evermore ferocious cyberattacks.

Take, for example, the SolarWinds hack in December 2020, arguably one of the largest on record, or the recent exploits that hit Exchange servers and affected tens of thousands of customers. Alongside such attacks, we've seen new impetus behind the regulation of artificial intelligence (AI), with the world's first regulatory framework for the technology arriving in April 2021. The EU's landmark proposals build on GDPR legislation, carrying heavy penalties for enterprises that fail to consider the risks and ensure that trust goes hand in hand with success in AI.

Altogether, a climate is emerging in which the significance of securing machine learning can no longer be ignored. Although this is a burgeoning field with much more innovation to come, the market is already starting to take the threat seriously.

Our research surveys reveal a steep change in deployments of machine learning during the pandemic, with more than 80 percent of enterprises saying they are trialing the technology or have put it into production, up from just over half a year ago.

But the topic of securing those systems has received little fanfare by comparison, even though research into the security of machine learning models goes back to the early 2000s.

We've seen several high-profile incidents that highlight the risks stemming from greater use of the technology. In 2020, a misconfigured server at Clearview AI, the controversial facial recognition start-up, leaked the company's internal files, apps and source code. In 2019, hackers were able to trick the Autopilot system of a Tesla Model S by using adversarial approaches involving sticky notes. Both pale in comparison to more dangerous scenarios, including the autonomous car that killed a pedestrian in 2018 and a facial recognition system that caused the wrongful arrest of an innocent person in 2019.

The security community is becoming more alert to the dangers of real-world AI. The CERT Coordination Center, which tracks security vulnerabilities globally, published its first note on machine learning risks in late 2019, and in December 2020, The Partnership on AI introduced its AI Incident Database, the first to catalog events in which AI has caused "safety, fairness, or other real-world problems".

The challenges that organizations are facing with machine learning are also shifting in this direction.

Several years ago, problems with preparing data, gaining skills and applying AI to specific business problems were the dominant headaches, but new topics are now coming to the fore. Among them are governance, auditability, compliance and above all, security.

According to CCS Insight's latest survey of senior IT leaders, security is now the biggest hurdle companies face with AI, cited by over 30 percent of respondents. Many companies struggle with the most rudimentary areas of security at the moment, but machine learning is a new frontier, particularly as business leaders start to think more about the risks that arise as the technology is embedded into more business operations.

Missing until recently are tools that help customers improve the security of their machine learning systems. A recent Microsoft survey, for example, found that 90 percent of businesses said they lack tools to secure their AI systems and that security pros were looking for specific guidance in the field.

Responding to this need, the market is now stepping up. In October 2020, non-profit organization MITRE, in collaboration with 12 firms including Microsoft, Airbus, Bosch, IBM and Nvidia, released an Adversarial ML Threat Matrix, an industry-focused open framework to help security analysts detect and respond to threats against machine learning systems.

Additionally, in April 2021, Algorithmia, a supplier of an enterprise machine learning operations (MLOps) platform that specializes in the governance and security of the machine learning life cycle, released a host of new security features focused on the integration of machine learning into the core IT security environment. They include support for proxies, encryption, hardened images, API security and auditing and logging. The release is an important step, highlighting my view that security will become intrinsic to the development, deployment and use of machine learning applications.

Finally, just last week, Microsoft released Counterfit, an open-source automation tool for security testing AI systems. Counterfit helps organizations conduct AI security risk assessments to ensure that algorithms used in businesses are robust, reliable and trustworthy. The tool enables pen testing of AI systems, vulnerability scanning and logging to record attacks against a target model.

These are early but important first steps that indicate the market is starting to take security threats to AI seriously. I encourage machine learning engineers and security professionals to get going begin to familiarize yourselves with these tools and the kinds of threats your AI systems could face in the not-so-distant future.

As machine learning becomes part of standard software development and core IT and business operations in the future, vulnerabilities and new methods of attack are inevitable. The immature and open nature of machine learning makes it particularly susceptible to hacking and that's why I predicted last year that we would see security become the top priority for enterprises' investment in machine learning by 2022.

A new category of specialism will emerge devoted to AI security and posture management. It will include core security areas applied to machine learning, like vulnerability assessments, pen testing, auditing and compliance and ongoing threat monitoring. In future, it will track emerging security vectors such as data poisoning, model inversions and adversarial attacks. Innovations like homomorphic encryption, confidential machine learning and privacy protection solutions such as federated learning and differential privacy will all help enterprises navigate the critical intersection of innovation and trust.

Above all, it's great to see the industry beginning to tackle this imminent problem now. Matilda Rhode, Senior Cybersecurity Researcher at Airbus, perhaps captures this best when she states, "AI is increasingly used in industry; it is vital to look ahead to securing this technology, particularly to understand where feature space attacks can be realized in the problem space. The release of open-source tools for security practitioners to evaluate the security of AI systems is both welcome and a clear indication that the industry is taking this problem seriously".

I look forward to tracking how enterprises progress in this critical field in the months ahead.

Nick McQuire, Chief of Enterprise Research, CCS Insight

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Hardening AI: Is machine learning the next infosec imperative? - ITProPortal