Deep Learning – Limitations and its future

Deep Learning – Limitations and its future

Introduction

The origin of this blog post is the recent debate spurred off Elon Musk and Mark Zuckerberg, on whether AI is good or bad for humanity.

Elon is an inspiration to many of us around the world, especially for anyone entrepreneurial, and also for us – the machine learning enthusiasts; self-driving cars, and his thoughts on AI and its applications in his companies (e.g., Tesla autonomous driving via its auto-pilot capabilities).

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Auto-pilot in action, in a Tesla Model S

But sometimes, I tend to differ with his point-of-views on certain topics. For ex., We have to leave earth and go to Mars, to sustain humanity (Space X was founded primarily to make this possible sooner), and that robots with Super-intelligence, will take over the planet soon. Elon is a visionary, and like Stephen Hawking, he too believes that AI could one day supersede humans. He is right; or rather, he ‘could’ be right. But the point I would like to make in this blog post is that AI and deep learning in particular, is at a very nascent stage now, and considering the capabilities that we have built into AI systems so far, I am pretty sure that the doomsday type scenario that Elon points out, is definitely not in the near future.

As Andrew NG, the cofounder of Coursera and former chief scientist at Chinese technology powerhouse Baidu, recently pointed out in a Harvard Business Review event, the more immediate problem we need to address, is job displacement, due to automation, and this is an area that we must focus on, rather than being distracted by science fiction-ish, dystopian elements.

Limitations of Deep learning

At the recently held AI By The Bay conference, Francois Chollet, an AI Researcher at Google and inventor of the widely used deep learning library Keras, spoke about the limitations of deep learning. He said that deep learning is simply a more powerful pattern recognition system when compared to previous statistical and machine learning methods. “The most important problem for A.I today is abstraction and reasoning”, said Chollet.

Current supervised perception and reinforcement learning algorithms require lots of data, they’re terrible at planning, and are merely doing straightforward pattern recognition. However, by contrast, humans are able to learn from very few examples and can do very long-term planning. Also, we are capable of forming abstract models of a situation and manipulate these models to achieve “extreme generalization”.

Lets take an example of how difficult it is to teach simple humans behaviours to a deep learning algorithm. Lets examine the task of not being hit by a car as you attempt to cross a road. In case of supervised learning, we would need huge datasets of this (vehicular movement) situations with clearly labeled actions to take, such as “stop” or “move”. Then you’d need to train a neural network to learn the mapping between the situation and the appropriate response action. If we go with the reinforcement learning route, where we give an algorithm a goal and then let it independently determine the appropriate actions to take, the computer would need to die thousands of times before it learns to avoid vehicles in different situations. In summary, humans only need to be told once to avoid cars. We’re equipped with the ability to generalize from just a few examples and are capable of imagining (modeling) the dire consequences of being run-over by a vehicle. And so without ever (in most cases) losing our life or hurting us significantly, most of us quickly learn to avoid being run over by motor vehicles.

Talking of anthropomorphizing machine learning models, Francois Chollet in his recent blog post, has a very interesting observation:

“A fundamental feature of the human mind is our “theory of mind”, our tendency to project intentions, beliefs and knowledge on the things around us. Drawing a smiley face on a rock suddenly makes it “happy”—in our minds. Applied to deep learning, this means that when we are able to somewhat successfully train a model to generate captions to describe pictures, for instance, we are led to believe that the model “understands” the contents of the pictures, as well as the captions it generates. We then proceed to be very surprised when any slight departure from the sort of images present in the training data causes the model to start generating completely absurd captions.”

Another difference between how we, humans, interpret our surrounding, versus how these models do, is ‘extreme generalisation’ that we are good at, versus the ‘local generalisation’ that the machine learning models can do.

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Can humans’s ‘extreme generalisation’ abilities be ported into a machine learning model?     (Picture source: https://blog.keras.io/the-limitations-of-deep-learning.html)

Lets take an example to understand this difference. If we take a young and smart 6 year old boy from Bangalore, and leave him in the town of Siem Reap in Cambodia, he will, in a few hours, manage to find out place to eat, and start communicating with the people around, and make his ends meet in a couple of days time. This ability to handle new situations, when we have never experience a similar one before – language, people, surroundings, etc., that is to perform abstraction and reasoning, far beyond what we have experienced so far, is arguably the defining characteristic of human cognition. In other words, this is “extreme generalization”; our ability to adapt to completely new, never experienced before situations, using very little data or even no new data at all. This is in sharp contrast with what deep/neural nets can do, which can be referred to as “local generalization”; that is, the mapping from inputs to outputs performed by deep nets quickly start to fall apart, if the new inputs differ even slightly from what they were trained with.

How Deep learning should evolve

A necessary transformational development that we can expect in the field of machine learning is a move away from models that merely perform pattern recognition and can only achieve local generalization, towards models capable of abstraction and reasoning, that can achieve extreme generalization. Whilst moving towards this goal, it will also be important for the models to require minimal intervention from human engineers. Today, most of the AI programs that are capable of basic reasoning, are all written by human programmers; for example the software that relies on search algorithms. All this will result in deep learning models which are not heavily dependant on supervised learning, which is the case today, and truly become self-supervised and independent.

As Francois calls out in his blog post, “we will move away from having on one hand “hard-coded algorithmic intelligence” (handcrafted software) and on the other hand “learned geometric intelligence” (deep learning). We will have instead a blend of formal algorithmic modules that provide reasoning and abstraction capabilities, and geometric modules that provide informal intuition and pattern recognition capabilities. The whole system would be learned with little or no human involvement.”

Will this result in machine learning engineers losing jobs? Not really; engineers will move higher up in the value chain. These engineers will then start focusing on crafting complex loss functions to meet business goals and use cases, and spend more time understanding how the models they have built impact the digital ecosystems in which they are deployed. For ex., interact and understand the users that consume the model’s predictions and the sources that generate the model’s training data. Only the largest companies can afford to have their data scientists spend time in these areas.

Another area of development could be, the models becoming more modular, like how the advent of OOP (Object-oriented programming) helped in software-development, and how the concept of ‘functions” help in re-using key functionalities in a software program. This will steer the way for the models becoming re-usable. What we do today, in the lines of model reuse across different tasks, is to leverage pre-trained weights for models that perform common functions, like visual feature extraction (for image recognition). When we reach this stage, we would not only leverage previously learned features (submodel weights or hyperparameters), but also model architectures and training procedures. And as models become more like programs, we would start reusing program subroutines, like the functions and classes found in our regular programming languages today.

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(Picture source: http://www.astuta.com/how-artificial-intelligence-big-data-will-transform-the-workplace/)

Closing thoughts…

The result of these developments in the deep learning models, would be a system that attains the state of Artificial General Intelligence (AGI).

So, does all this, again take us to the debate i initially started with – a singularitarian robot apocalypse to takeover planet Earth? For now, and for the near-term future, I think its a pure fantasy, that originates from a profound misunderstanding of intelligence and technology.

This post began with Elon Musk’s comments; and I will end it with a recent comment from him – “AI will be the best of worst hing ever for humanity…”.

If you want to dwell deeper in this fantasy of Superintelligence, then do read Max Tegmark’s book Life 3.0 published last month. Elon himself, highly recommends it. :).

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Another classic is Nick Bostrom’s Superintelligence – Paths, Dangers, Strategies. And, if you are a Python programmer, and want to start building Deep Learning models, then I strongly recommend Francois Chollet’s book Deep Learning with Python

Further reading

Here are some recent research papers highlighting limitations of Deep Learning:

 

 

Cover image source: https://regmedia.co.uk/

AI powered Cyber Security startups

AI powered Cyber Security startups

Artificial Intelligence (AI) and Machine Learning have become mainstream these days, but at the same time, they are some of the most used (abused) term/jargon in the last 2-3 years.

Last year’s Gartner hype cycle report (2016 Hype Cycle for Emerging Technologies – shown below) shows this trend clearly.

emerging-tech-hc-2016.png;wa59f7b006c484099e.png

Why do we need AI in Cyber security

The biggest challenge in the Cybersecurity Threat Managment space today, is the ability (or lack of) of effective “detection” of cyber attacks. One of the key levers in making “detection” work is reducing the dependency on the “human” element in this entire threat management lifecycle:

  • Let it be the detection techniques (signatures, patterns, and for that matter ML models and their hyper-parameters), or,
  • The incident “response” techniques:
    • involving human security analysts for analysing the detections, or,
    • human security administrators to remediate/block the attacks at the network  or system level

Introducing automation and bringing in cognitive methods in each of these areas, is the only way forward, to take the adversaries head-on. And there has been numerous articles, presentations and whitepapers published on why Machine Learning (ML) and AI will play a key role in addressing the cyber threat management challenge.

In my pursuit of understanding how AI can be used effectively in the cybersecurity space, I have come across products developed by some of the leading startups in this domain. And in this blog post, I attempt to share my thoughts on 10 of these products, chosen primarily on their market cap/revenue, IP (intellectual property) potential, and any reference materials available about their successful detections so far.

Note:

  • I have tried to cover as much breadth I can, in terms of covering Products falling under various domains of Cybersecurity – Network detection, UEBA, Application security and Data security, and so there is a good chance I have missed some contenders in this area. AI in Cyber is a rapidly growing plateau, and I hope to cover more ground in the coming months.
  • These Products are listed below in no particular order.

Lets get started.

1. PatternEx

Founded 2013, San Jose, California
https://www.patternex.com/
@patternex

PatternEx’s Threat Prediction Platform is designed to create “virtual security analysts” that mimic the intuition of human security analysts in real time and at scale. The platform reportedly detects ten times more threats with five times fewer false positives compared with approaches based on Machine Learning-Anomaly Detection technology. Using a new technology called “Active Contextual Modeling” or ACM, the product synthesizes analyst intuition into predictive models. These models, when deployed across global customers, can reportedly learn from each other and achieve a network effect in detecting attack patterns.

The process of Active Contextual Modeling (ACM) facilitates communication between the artificial intelligence platform and the human analyst. Raw data is ingested, transformed into behaviors, and run through algorithms to find rare events for an analyst for review. After investigation, an appropriate label is attached to each event by the analyst. The system learns from these labels and automatically improves detection efficacy. Data models created though this process are flexible and adaptive. Event accuracy is continuously improved. Historic data is retrospectively analyzed as new knowlege is added to the system.

Training the AI happens when the AI presents a set of alerts to human analysts, who review the alerts and define them as attacks or not. The analyst applies a label to the alert which trains a supervised learning model that automatically adapts and improves. This is a trained AI, and interesting concept, that attempts to simulate a security analyst, helping the AI system to improve the detection over a period of time.

PatternEx was founded by Kalyan Veeramachaneni, Uday Veeramachaneni, Vamsi Korrapati, and Costas Bassias.

PatternEx has received funding of about $7.8M so far.

2. Vectra Networks

Founded 2011, USA
http://www.vectranetworks.com/
@Vectra_Networks

Vectra Networks’ platform is designed to instantly identify cyber attacks while they are happening as well as what the attacker is doing. Vectra automatically prioritizes attacks that pose the greatest business risk, enabling organizations to quickly make decisions on where to focus their time and resources. The company says that platform uses next-generation compute architecture and combines data analytics and machine learning to detect attacks on every device, application and operating system. And to do this, the system uses the most reliable source of information – network traffic. Logs only provide low-fidelity summaries of events that have already been seen, not what has been missed. Likewise, endpoint security is easy to compromise during an active intrusion.

The Vectra Networks approach to threat detection blends human expertise with a broad set of data science and machine learning techniques. This model, known as Automated Threat Management, delivers a continuous cycle of threat intelligence and learning based on cutting-edge research, global learning models, and local learning models. With Vectra, all of these different perspectives combine to provide an ongoing, complete and integrated view that reveals complex multistage attacks as they unfold inside your network.

They have an interesting approach to use Supervised and Unsupervised ML models to detect cyber attacks. They have a “Global Learning” element, where supervised ML algorithms are used to build models to detect “generic” and “new known” attack patterns. “Local learning” element uses Unsupervised ML algorithms are used to collect knowledge of local norms in an enterprise, and then detecting deviations from those norms.

Vectra networks has received funding of about $87M so far, and has seen very good traction in the Enterprise Threat Detection space, where ML models are a lot more effective than using conventional signature/pattern based detections.

3. Darktrace

Founded 2013, UK
https://www.darktrace.com/
@Darktrace

Darktrace is inspired by the self-learning intelligence of the human immune system; it’s Enterprise Immune System technology iteratively learns a pattern of life for every network, device and individual user, correlating this information in order to spot subtle deviations that indicate in-progress threats. The system is powered by machine learning and mathematics developed at the University of Cambridge. Some of the world’s largest corporations rely on Darktrace’s self-learning appliance in sectors including energy and utilities, financial services, telecommunications, healthcare, manufacturing, retail and transportation.

DarkTrace has a set of products, which use ML and AI in detecting and blocking cyber attacks:

DarkTrace (Core) is the Enterprise Immune System’s flagship threat detection and defense capability, based on unsupervised machine learning and probabilistic mathematics. It works by analyzing raw network data, creating unique behavioral models for every user and device, and for the relationships between them.

The Threat Visualizer is Darktrace’s real-time, 3D threat notification interface. As well as displaying threat alerts, the Threat Visualizer provides a graphical overview of the day-to-day activity of your network(s), which is easy to use, and accessible for both security specialists and business executives.

Darktrace ICS retains all of the capabilities of Darktrace in the corporate environment, creating unique, behavioral understanding of the ‘self’ for each user and device within an Industrial Control systems’s network, and detecting threats that cannot be defined in advance by identifying even subtle shifts in expected behavior in the OT space.

Darktrace Antigena is capable of taking a range of measured, automated actions in the face of confirmed cyber-threats detected in real time by Darktrace. Because Darktrace understands the ‘pattern of life’ of users, devices, and networks, Darktrace Antigena is able to take action in a highly targeted manner, mitigating threats while avoiding over-reactions. It basically performs three steps, once a cyber attack is detected by the DarkTrace Core:

  • Stop or slow down activity related to a specific threat
  • Quarantine or semi-quarantine people, systems, or devices
  • Mark specific pieces of content, such as email, for further investigation or tracking

DarkTrace has received funding of about $105M so far.

4. Status today

Founded 2015, UK
http://www.statustoday.com/
@statustodayhq

StatusToday was founded by Ankur Modi and Mircea Danila-Dumitrescu. It is a SaaS based AI-powered Insights Platform that understands human behavior in the workplace, helping organizations ensure security, productivity and communication.
Through patent-pending AI that understands human behavior, StatusToday maps out human threats and key behavior patterns internal to the company.

In a nutshell, this product collects all the user activity log data, from various IT systems, applications, servers and even everyday cloud services like google apps or dropbox. After collecting this metadata, the tool extracts as many functional parameters as possible and present them in easily understood reports graph. I think they use one of the Link analysis ML models to plot the relationship between all these user attributes.

The core solution provides direct integrations with Office 365, Exchange, CRMs, Company Servers and G-Suite (upcoming) to enable a seamless no-effort Technology Intelligence Center.

StatusToday has been identified as one of UK’s top 10 AI startups by Business Insider, TechWorld, VentureRadar and other forums, in the EU region.

Status Today has received funding of about $1.2M so far.

5. Jask

Founded 2015, USA
http://jask.io/
@jasklabs

Jask aims to use AI in solving the age old problem of tsunami of logs fed into SIEM tools which then generate events & alerts, and other indicators that security analysts face every day, which produce a never ending flood of unknowns which forces these analysts to spend their valuable time sorting through indicators in the endless hunt for real threats.

At the heart is their product Trident, which is a big data platform for real time and historical analysis over an unlimited amount of stored security telemetry data. Trident collects all this data directly from the network and complements that with the ability to fuse other data sources such as threat intelligence (through STIX and TAXII), providing context into real threats. Once Trident identifies a sequence that indicates an attack, it generates SmartAlerts, which analysts can use to have the full picture of an attack, also allowing them to spend their time on real analysis instead of an endless hunt for the attack story.

They have really interesting blog posts on their site, which are worth a read.

Jask has received funding of about $2M so far.

6. Fortscale

Founded 2012, Israel
https://fortscale.com/
@fortscale

Fortscale uses a machine learning system to detect abnormal account behavior indicative of credential compromise or abuse. The company was founded by security engineers from the Israeli Defense Force’s elite security unit. The products key ability is to rapidly detect and eliminate insider threats. From rogue employees to hackers with stolen credentials, Fortscale is designed to automatically and dynamically identify anomalous behaviors and prioritizes the highest-risk activities within any application, anywhere in the enterprise network.

Behavioral data is automatically ingested from SIEM tools and enriched with contextual data, and multi-dimensional baselines are created autonomously and statistical analysis reveals any deviations, which are then captured in SMART Alerts. All of this can viewed and analysed in Fortscale Console.

Fortscale was named Gartner Cool Vendor (2016) in the UEBA< Fraud Detection and User Authentication category.

More info about the product can be found here.

Fortscale has received funding of about $40 million so far.

7. Neokami

Founded 2014, Germany & USA
https://www.neokami.com/
@neokami_tech

Neokami attempts to tackle a very important problem we all face today – keeping a track of where all our and an enterprises’s sensitive information resides. Neokami’s CyberVault uses AI to discover, secure and govern Sensitive Data in the cloud, on premise, or across their physical assets. It can also scan images to detect sensitive information, as it uses highly optimized NLP for text analytics & Convolutional Neural Networks for image data analytics.
In a nutshell, Neokami uses a multi-layer decision pipeline, wherein it takes in data stream or files, and performs pattern matching, text analytics, image recognition, N-gram modelling and topic detection, using ML learning methods like Random Forest, to learn user-specific sensitivity over time. Post this analysis, a % sensitivity Score is generated and assigned to the data, which can then be picked up for further analysis and investigation.

Some key use cases Neokami tackles are – isolating PII to meet regulations such as GDPR, HIPPA, etc., discovering a company’s confidential information and intellectual property, scan images for sensitive information, protect information in Hadoop clusters, cloud, endpoints or mainframes.

Neokami was acquired by Relayr in Feb this year, and has received $1.1million funding so far, from three investors.

8. Cyberlytic

Founded 2013, UK
https://www.cyberlytic.com/
@CyberlyticUK

Cyberlytic call themselves the ‘Intelligent Web application security’ product. Their elevator pitch is they provide advanced web-application security using AI to classify attack data, identify threat characteristics and prioritize high-risk attacks.

The founders have had a stint with the UK Ministry of Defense, where this product was first used and has been in use support critical cybersecurity research projects in the department.

Cyberlytic analyzes web server traffic in real-time, and determines the sophistication, capability and effectiveness of each attack. This information is translated into a risk score, to prioritize incident response and prevent dangerous web attacks. And the underlying ML models adapt to new and evolving threats without requiring the creation or management of firewall rules. They key to their detection, is their patented ML classification approach, which appears to be more effective in detecting web application attacks than the conventional signature/pattern based detection.

Cyberlytic is a combination of two products – the Profiler, and the Defender. The Profiler provides real-time risk assessment of web-based attacks, by connecting to the web server and analyzing web traffic, to determine the capability, sophistication and effectiveness of each attack. And Defender, is deployed on web servers, and acts on the assessment performed by Profiler, by blocking and preventing web-based cyber-attacks from reaching critical web applications or the underlying data layer.

Cyberlytic has also been gaining a lot of attention in the UK and EU region; Real Business, an established publication in the UK, has named Cyberlytic as one of the UK’s 50 most disruptive tech companies in 2017.

Cyberlytic has received funding of about $1.24 million.

9. harvest.ai

Founded 2014, USA
http://www.harvest.ai/
@harvest_ai

Harvest.ai aims at detecting and stopping data breaches, by using AI-based algorithms to learn the business value of critical documents across an organization, and offer what it describes as an industry-first ability to detect and stop data breaches. In a nutshell, Harvest.ai is an AI powered advanced DLP system having the ability to perform UEBA.

Key features of their product MACIE, includes:

  • Use AI to track intellectual property across an organization’s network, including emails and other content derived from IP.
  • MACIE understands the business value of all data across a network and whether it makes sense for a user to be accessing certain documents, a key indicator of a targeted attack.
  • MACIE can automatically identify risk to the business of data that is being exposed or shared outside the organization and remediate based on policies in near real-time. It not only classifies documents but can identify true IP matches to protect sensitive documents that exist for an organization, whether it be technology, brand marketing campaigns or the latest pharmaceutical drug.
  • MACIE not only detects changes in a single users behavior, but it has the unique ability to detect minor shifts in groups of users, which can indicate an attack.

Their blog has some interesting analysis of some of the recent APT attacks, and how MACIE detected them. Definitely work a read.

Harvest.ai has received funding of about $2.71 million so far, and interestingly, they have been acquired by Amazon in Jan this year, for reportedly $20 million.

10. Deep Instinct

Founded 2014, Israel
http://www.deepinstinct.com/
@DeepInstinctSec

Deep Instinct focuses as End point as the pivot point, in detecting and blocking cyber attacks, and thus fall under the category of EDR. There is something going on in israel, for the last few years, as many cybersecurity startups (Cyberreason, Demisto, Intsights, etc.) are being founded by ex-IDF engineers in Israel, and a good portion of these startups are to do with Endpoint Detection and Response (EDR).

Deep Instinct uses deep learning to detect unknown malware in real-time, just by analysing the binary raw details of the binary picked up by the system. The software runs efficiently on the combination of central processing units (CPUs) and graphics processing units (GPUs) and Nvidia’s CUDA software for running non-graphics software on graphics chips. The GPUs enable the company to do in a day what would take three months for a CPU.

I couldn’t find enough documentation on their website to understand how this deep learning system actually works, but their website has a link to register for an online demo. So it must be definitely worth a try.

They are also gaining a lot of attention in the EDR space, and NVIDIA has selected Deep Instinct as one of the 5 most disruptive AI startups this year.

Deep Instinct has raised $50 million so far, from Blumberg Capital, UST Global, CNTP, and Cerracap.