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The worlds of artificial intelligence and cybersecurity have become deeply entwined in recent years, as organizations work to keep up with and ideally block increasingly sophisticated malicious hackers. Today, a startup thats built a deep learning solution that it claims can both identify and stop even viruses that have yet to be identified has raised a large round of funding from some big strategic partners.

Deep Instinct, which uses deep learning both to learn how to identify and stop known viruses and other hacking techniques, as well as to be able to identify completely new approaches that have not been identified before, has raised $43 million in a Series C.

The funding is being led by Millennium New Horizons, with Unbound (a London-based investment firm founded by Shravin Mittal), LG and Nvidia all participating. The investment brings the total raised by Deep Instinct to $100 million, with HP and Samsung among its previous backers. The tech companies are all strategics, in that (as in the case of HP) they bundle and resell Deep Instincts solutions, or use them directly in their own services.

The Israeli-based company is not disclosing valuation, but notably, it is already profitable.

Targeting as-yet unknown viruses is becoming a more important priority as cybercrime grows. CEO and founder Guy Caspi notes that currently there are more than350,000 new machine-generated malware created every day with increasingly sophisticated evasion techniques, such as zero-days and APTs (Advanced Persistent Threats). Nearly two-thirds of enterprises have been compromised in the past year by new and unknown malware attacks originating at endpoints, representing a 20% increase from the previous year, he added. And zero-day attacks are now four times more likely to compromise organizations. Most cyber solutions on the market cant protect against these new types of attacks and have therefore shifted to a detect-response approach, he said, which by design means that they assume a breach will happen.

While there is already a large profusion of AI-based cybersecurity tools on the market today, Caspi notes that Deep Instinct takes a critically different approach because of its use of deep neural network algorithms, which essentially are set up to mimic how a human brain thinks.

Deep Instinct is the first and currently the only company to apply end-to-end deep learning to cybersecurity, he said in an interview. In his view, this provides a more advanced form of threat protection than the common traditional machine learning solutions available in the market, which rely on feature extractions determined by humans, which means they are limited by the knowledge and experience of the security expert, and can only analyze a very small part of the available data (less than 2%, he says). Therefore, traditional machine learning-based solutions and other forms of AI have low detection rates of new, unseen malware and generate high false-positive rates. Theres been a growing body of research that supports this idea, although weve not seen many deep learning cybersecurity solutions emerge as a result (not yet, anyway).

He adds that deep learning is the only AI-basedautonomous system that can learn from any raw data, as its not limited by an experts technological knowledge. In other words, its not based just on what a human inputs into the algorithm, but is based on huge swathes of big data, sourced from servers, mobile devices and other endpoints, that are input in and automatically read by the system.

This also means that the system can be used in turn across a number of different end points. Many machine learning-based cybersecurity solutions, he notes, are geared at Windows environments. That is somewhat logical, given that Windows and Android account for the vast majority of attacks these days, but cross-OS attacks are now on the rise.

While Deep Instinct specializes in preventing first-seen, unknown cyberattacks like APTs and zero-day attacks, Caspi notes that in the past year there has been a rise in both the amount and the impact of cyberattacks covering other areas. In 2019, Deep Instinct saw an increase in spyware and ransomware on top of an increase in the level of sophistication of the attacks that are being used, specifically with more file-less attacks using scripts and powershell, living off the land attacks and the use of weaponized documents like Microsoft Office files and PDFs. These sit alongside big malware attacks like Emotet, Trickbot, New ServeHelper and Legion Loader.

Today the company sells services both directly and via partners (like HP), and its mainly focused on enterprise users. But since there is very little in the way of technical implementation (Our solution is mostly autonomous and all processes are automated [and] deep learning brain is handling most of the security, Caspi said), the longer-term plan is to build a version of the product that consumers could adopt, too.

With a large part of antivirus software often proving futile in protecting users against attacks these days, that could come as a welcome addition to the market, despite how crowded it already is.

There is no shortage of cybersecurity software providers, yet no company aside from Deep Instinct has figured out how to apply deep learning to automate malware analysis, said Ray Cheng, partner at Millennium New Horizons, in a statement. What excites us most about Deep Instinct is its proven ability to use its proprietary neural network to effectively detect viruses and malware no other software can catch. That genuine protection in an age of escalating threats, without the need of exorbitantly expensive or complicated systems is a paradigm change.

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Deep Instinct nabs $43M for a deep-learning cybersecurity solution that can suss an attack before it happens - TechCrunch

Image: ShutterstockThe 2020 outlook for Asset Management re-affirms impact of globalization and outperformance of private equity. While the developed worlds economy has sent mixed signals, all eyes are now on Asia and especially India, to drive the next phase of growth. The goal is to provide Investment Solutions for its mix of young as well as senior population. Its diversity cultural, economic, regional & regulatory, will pose the next challenge.

The application of Data Science & Machine Learning has delivered value for portfolio managers through quick and uniform decision-making. Strategic Beta Funds which have consistently generated added value, rely heavily on the robustness of their portfolio creation models which are excruciatingly data driven. Deploying Machine Learning algorithms helps assess credit worthiness of firms and individuals for lending and borrowing. Data Science and Machine Learning solutions eliminate human bias and calculation errors while evaluating investments in an optimum period.

Investment management is justified as an industry only to the extent that it can demonstrate a capacity to add value through the design of dedicated investor-centric investment solutions, as opposed to one-size-fits-all manager-centric investment products. After several decades of relative inertia, the much needed move towards investment solutions has been greatly facilitated by a true industrial revolution taking place in investment management, triggered by profound paradigm changes with the emergence of novel approaches such as factor investing, liability-driven and goal-based investing, as well as sustainable investing. Data science is expected to play an increasing role in these transformations.

This trend poses a critical challenge to global academic institutions: educating a new breed of young professionals and equipping them with the right skills to address the situation, and who could seize the fast-developing new job opportunities in this field. Continuous education gives the opportunity to meet with new challenges of this ever-changing world, especially in the investment industry.

As recently emphasized by our colleague Vijay Vaidyanathan, CEO, Optimal Asset Management, former EDHEC Business School PHD student, and online course instructor at EDHEC Business School, our financial well-being is second only to our physical well-being, and one of the key challenges we face is to enhance financial expertise. To achieve this, we cannot limit ourselves to the relatively small subset of the population who can afford to invest the significant time and expense of attending a formal, full-time degree programme on a university campus. Therefore, we must find ways to elevate the quality of financial professional financial education to ensure that all asset managers and asset owners are fully equipped to make intelligent and well-informed investment decisions.

Data science applied to asset management, and education in the field, is expected to affect not only investment professionals but also individuals. On this topic, we would like to share insights from Professor John Mulvey, Princeton University, who is also one of EDHEC on-line course instructors. John believes that machine learning applied to investment management is a real opportunity to assist individuals with their financial affairs in an integrated manner. Most people are faced with long-term critical decisions about saving, spending, and investing to achieve a wide variety of goals.

These decisions are often made without much professional guidance (except for wealthier clients), and without much technical training. Current personalized advisors are reasonable initial steps. Much more can be done in this area with modern data science and decision-making tools. Plus, younger people are more willing to trust fully automated computational systems. This domain is one of the most relevant and significant areas of development for future investment management.

By Nilesh Gaikwad, EDHEC Business School country manager in India, and Professor Lionel Martellini, EDHEC-Risk Institute Director.

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How Machine Learning Will Reshape The Future Of Investment Management - Forbes India

Three quarters of Greater Manchester's digital tech businesses have experienced significant growth in the last 12 months

New figures from Manchester Digital, the independent trade body for digital and tech businesses in Greater Manchester, have revealed that 72% of businesses in the region have experienced growth in the last year, up from 54% in 2018.

Despite such prosperous results, companies are still calling out for talent, with developer roles standing out as the most in-demand for the seventh consecutive year. The other most sought-after skills in the next three years include data science (15%), UX (15%), and AI and machine learning (11%).

In the race to acquire top talent, almost 25% of Manchester vacancies advertised in the last 12 months remained unfilled, largely due to a lack of suitable candidates and inflated salary demands.

Unveiled at Manchester Digitals annual Skills Festival last week, the Annual Skills Audit, which evaluates data from 250 digital and tech companies and employees across the region, also analysed the various professional pathways into the sector.

The majority (77%) of candidates entering the sector harbour a degree of some sort; however, of the respondents who possessed a degree, almost a quarter claimed it was not relevant to tech, while a further 22% reported traversing through the sector from another career.

In other news: Jisc report calls for an end to pen and paper exams by 2025

On top of this, almost one in five respondents said they had self-taught or upskilled their way into the sector a positive step towards boosting diversity in terms of both the people and experience pools entering the sector.

Its positive to see a higher number of businesses reporting growth this year, particularly from SMEs. While the political and economic landscape is by no means settled, it seems that businesses have strategies in place to help them navigate through this uncertainty, said Katie Gallagher, managing director of Manchester Digital.

Whats particularly interesting in this years audit are the data sets around pathways into the tech sector, added Gallagher. While a lot of people still do report having degrees and wed like to see more variation here in terms of more people taking up apprenticeships, work experience placements etc. its interesting to see that a fair percentage are retraining, self-training or moving to the sector with a degree thats not directly related. Only by creating a talent pool from a wide and diverse range of people and backgrounds can we ensure that the sector continues to grow and thrive sustainably.

When asked what they liked about working for their current employer, employees across the region mentioned flexible work as the number one perk they value (40%). Career progression was also a crucial factor to those aged 18-21, with these respondents also identifying brand prestige as a reason to choose a particular employer.

For this first time this year, weve expanded the Skills Audit to include opinions from employees, as well as businesses. With the battle for talent still one of the biggest challenges employers face, were hoping that this part of the data set provides some valuable insights into why people choose employers and what they value most and consequently helps businesses set successful recruitment and retention strategies, Gallagher concluded.

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Manchester Digital unveils 72% growth for digital businesses in the region - Education Technology

TURIN, Italy--(BUSINESS WIRE)--The European Central Bank (ECB), in collaboration with Reply, leader in digital technology innovation, is organising the Supervisory Data Hackathon, a coding marathon focussing on the application of Machine Learning and Artificial Intelligence.

From 27 to 29 February 2020, at the ECB in Frankfurt, more than 80 participants from the ECB, Reply and further companies explore possibilities to gain deeper and faster insights into the large amount of supervisory data gathered by the ECB from financial institutions through regular financial reporting for risk analysis. The coding marathon provides a protected space to co-creatively develop new ideas and prototype solutions based on Artificial Intelligence within a short timeframe.

Ahead of the event, participants submit projects in the areas of data quality, interlinkages in supervisory reporting and risk indicators. The most promising submissions will be worked on for 48 hours during the event by the multidisciplinary teams composed of members from the ECB, Reply and other companies.

Reply has proven its Artificial Intelligence and Machine Learning capabilities with numerous projects in various industries and combines this technological expertise with in-depth knowledge of the financial services industry and its regulatory environment.

Coding marathons using the latest technologies are a substantial element in Replys toolset for sparking innovation through training and knowledge transfer internally and with clients and partners.

ReplyReply [MTA, STAR: REY] specialises in the design and implementation of solutions based on new communication channels and digital media. As a network of highly specialised companies, Reply defines and develops business models enabled by the new models of big data, cloud computing, digital media and the internet of things. Reply delivers consulting, system integration and digital services to organisations across the telecom and media; industry and services; banking and insurance; and public sectors. http://www.reply.com

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REPLY: European Central Bank Explores the Possibilities of Machine Learning With a Coding Marathon Organised by Reply - Business Wire

AI and Predictive Analytics: Myth, Math, or Magic?

Don't fall into the trap of thinking that math-based analytics can predict human behavior with certainty.

We are a species invested in predicting the future -- as if our lives depended on it. Indeed, good predictions of where wolves might lurk were once a matter of survival. Even as civilization made us physically safer, prediction has remained a mainstay of culture, from the haruspices of ancient Rome inspecting animal entrails to business analysts dissecting a wealth of transactions to foretell future sales.

Such predictions generally disappoint. We humans are predisposed to assuming that the future is a largely linear extrapolation of the most recent (and familiar) past. This is one -- or a combination -- of the nearly 200 cognitive biases that allegedly afflict us.

A Prediction for the Coming Decade

With these caveats in mind, I predict that in 2020 (and the decade ahead) we will struggle if we unquestioningly adopt artificial intelligence (AI) in predictive analytics, founded on an unjustified overconfidence in the almost mythical power of AI's mathematical foundations. This is another form of the disease of technochauvinism I discussed in a previous article.

Science fiction author and journalist Cory Doctorow's article, "Our Neophobic, Conservative AI Overlords Want Everything to Stay the Same," in the Los Angeles Review of Books, offers a succinct and superb summary of technochauvinism as it operates in AI. "Machine learning," he asserts, "is about finding things that are similar to things the machine learning system can already model." These models are, of course, built from past data with all its errors, gaps, and biases.

The premise that AI makes better (e.g., less biased) predictions than humans is already demonstrably false. Employment screening apps, for example, are often riddled with a bias toward hiring white males because the historical hiring data used to train its algorithms consisted largely of information about hiring such workers.

The widespread belief that AI can predict novel aspects of the future is simply a case of magical thinking. Machine learning is fundamentally conservative, based as it is on correlations in existing data; its predictions are essentially extensions of the past. AI lacks the creative thinking ability of humans. Says Tabitha Goldstaub, a tech entrepreneur and commentator, about the use of AI by Hollywood studios to decide which movies to make: "Already we're seeing that we're getting more and more remakes and sequels because that's safe, rather than something that's out of the box."

A Predictive Puzzle

AI, together with the explosion of data available from the internet, have raised the profile of what used to be called operational BI, now known as predictive analytics and its more recent extension into prescriptive analytics. Attempting to predict the future behavior of prospects and customers and, further, to influence their behavior is central to digital transformation efforts. Predictions based on AI, especially in real-time decision making with minimal human involvement, require careful and ongoing examination lest they fall foul of the myth of an all-knowing AI.

As Doctorow notes, AI conservatism arises from detecting correlations within and across existing large data sets. Causation -- a much more interesting feature -- is more opaque, usually relying on human intuition to separate the causal wheat from the correlational chaff, as I discussed in a previous Upside article.

Nonetheless, causation can be separated algorithmically from correlation in specific cases, as described by Mollie Davies and coauthors. I cannot claim to follow the full mathematical formulae they present, but the logic makes sense. As the authors conclude, "Instead of being naively data driven, we should seek to be causal information driven. Causal inference provides a set of powerful tools for understanding the extent to which causal relationships can be learned from the data we have." They present math that data scientists should learn and apply more widely.

However, there is a myth here, too: that predictive (and prescriptive) analytics can divine human intention, which is the true basis for understanding and influencing behavior. As Doctorow notes, in trying to distinguish a wink from a twitch, "machine learning [is not] likely to produce a reliable method of inferring intention: it's a bedrock of anthropology that intention is unknowable without dialogue." Dialogue -- human-to-human interaction -- attracts little attention in digital business implementation.

The Dilemma of (Real) Prediction

Once accused of looking too intently in the rearview mirror, business intelligence has today embraced prediction and prescription as among its most important goals. Despite advances in data availability and math-based technology, truly envisaging future human intentions and actions remains a strictly human gift.

The myth that math-based analytics can predict human behavior with certainty is probably the most dangerous magical thinking we data professionals can indulge in.

About the Author

Dr. Barry Devlin defined the first data warehouse architecture in 1985 and is among the worlds foremost authorities on BI, big data, and beyond. His 2013 book, Business unIntelligence, offers a new architecture for modern information use and management.

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AI and Predictive Analytics: Myth, Math, or Magic? - TDWI