Emma Woollacott02 June 2020 at 13:14 UTC Updated: 02 June 2020 at 14:48 UTC

ML security evasion event is based on a similar competition held at DEF CON 27 last summer

The defensive capabilities of machine learning (ML) systems will be stretched to the limit at a Microsoft security event this summer.

Along with various industry partners, the company is sponsoring a Machine Learning Security Evasion Competition involving both ML experts and cybersecurity professionals.

The event is based on a similar competition held at AI Village at DEF CON 27 last summer, where contestants took part in a white-box attack against static malware machine learning models.

Several participants discovered approaches that completely and simultaneously bypassed three different machine learning anti-malware models.

The 2020 Machine Learning Security Evasion Competition is similarly designed to surface countermeasures to adversarial behavior and raise awareness about the variety of ways ML systems may be evaded by malware, in order to better defend against these techniques, says Hyrum Anderson, Microsofts principal architect for enterprise protection and detection.

The competition will consist of two different challenges. A Defender Challenge will run from June 15 through July 23, with the aim of identifying new defenses to counter cyber-attacks.

The winning defensive technique will need to be able to detect real-world malware with moderate false-positive rates, says the team.

Next, an Attacker Challenge running from August 6 through September 18 provides a black-box threat model.

Participants will be given API access to hosted anti-malware models, including those developed in the Defender Challenge.

RECOMMENDED DEF CON 2020: Safe Mode virtual event will be free to attend, organizers confirm

Contestants will attempt to evade defenses using hard-label query results, with samples from final submissions detonated in a sandbox to make sure theyre still functional.

The final ranking will depend on the total number of API queries required by a contestant, as well as evasion rates, says the team.

Each challenge will net the winner $2,500 in Azure credits, with the runner up getting $500 in Azure credits.

To win, researchers must publish their detection or evasion strategies. Individuals or teams can register on the MLSec website.

Companies investing heavily in machine learning are being subjected to various degrees of adversarial behavior, and most organizations are not well-positioned to adapt, says Anderson.

It is our goal that through our internal research and external partnerships and engagements including this competition well collectively begin to change that.

READ MORE Going deep: How advances in machine learning can improve DDoS attack detection

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Microsoft throws weight behind machine learning hacking competition - The Daily Swig

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GBG(AIM:GBG), the global technology specialist in fraud and compliance management, identity verification and location data intelligence, today announced its expansion ofAI and machine learningcapabilities for its transaction and payment monitoring solution,Predator, making deep learning and predictive analytics available to their entire digital risk management customer journey. GBG first announced its machine learning capabilities forInstinct Hub, their digital onboarding fraud management system in January this year. The new AI capability additionally processes third party data -- device fingerprinting, geolocation, mobile and IP, endpoint threat intelligence, behavioral analytics -- assimilated into the GBG Digital Risk Management and Intelligence platform to enhance their model performance in fraud detection.

With the current pandemic giving rise to changes in consumer behavior in spending, fund transfers and loans, the ability to re-learn new data and adapt to new environments can help financial organizations detect emerging and escalating transaction and payment fraud trends and mitigate fraud loss. Based on GBG's "Understanding COVID-19 Fraud Risks" poll results in April, 37% of respondents see transaction fraud as the fraud typology that they are most vulnerable to.

"Fraud is irregular, complex and evolves dynamically. Standard fraud model deteriorates over time, exposing businesses to new fraud typologies and fraud losses. Through continual and autonomous model training in GBG Machine Learning, we address the issue of model deterioration," said June Lee, Managing Director, APAC, GBG.

"Today machine learning provides an average of 20% uplift in fraud detection, GBG Machine Learning has performed well to provide incremental alerts on missed frauds for our customers," adds Lee.

GBG Machine Learning utilizes Random Forest, Gradient Boosting Machine and Neural Networks -- three leading and proven algorithms for fraud detection. These algorithms embody strong predictive analytics, fast training models and high scalability, learning through both historical and new data. GBG AutoML (Automated Machine Learning) enables adaptive learning to provide the model capability to re-learn and update itself automatically based on a specified time interval.

"Through our APAC COVID-19 fraud risk poll results, digital retail banking services are growing in demand, from e-wallet, e-loan, digital onboarding, to digital credit card application; most respondents see a rise in e-banking services utilization. The ability to easily spot complex fraud and misused identities in first party bust outs and mule payments, high volume and high velocity frauds such as online banking account takeover and card not present frauds across both onboarding and ongoing customer payments becomes more pressing today," said Michelle Weatherhead, Operations Director, APAC, GBG.

"In addition, segments like SME lending and microfinancing would be able to harness machine learning to spot irregularity in borrower patterns by assimilating both identity, profile and behavioural type data. GBG Machine Learning is able to analyse large sums of data using algorithmic calculations on multiple features to determine fraud probability in greater accuracy," quips Dr Alex Low, Data Scientist, GBG.

GBG Machine Learning is designed to simplify machine learning deployment for both fraud managers and data scientists, removing the need to have a data scientist in-house or having to work back to back with the vendor to lower cost of operation. The solution offers high user controls from feature creations, model selection and configuration, results and analysis interpretation and alert thresholds. Users can also configure the solution to auto schedule and update new fraud patterns through its intuitive user interface with tool tips built in.

The solution takes a "white box" approach to provide an open and transparent modelling process for ease in model governance and meeting regulatory requirements. The machine learning score and top contributing features to results are visible to the users who need to gather further insights and understanding on the machine learning model performance and behaviours.

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GBG Predator With Machine Learning Simplifies And Improves Fraud Detection For Credit Card, Mobile, Digital Payments And Digital Banking Transactions...

A newly released tool makes light work of solving human verification challenges

F-Secure says its achieved 90% accuracy in cracking Microsoft Outlooks text-based CAPTCHAs using its AI-based CAPTCHA-cracking server, CAPTCHA22.

For the last two years, the security firm has been using machine learning techniques to train unique models that solve a particular CAPTCHA, rather than trying to build a one-size-fits-all model.

And, recently, it decided to try the system out on a CAPTCHA used by an Outlook Web App (OWA) portal.

The initial attempt, according to F-Secure, was comparatively unsuccessful, with the team finding that after manually labelling around 200 CAPTCHAs, it could only identify the characters with an accuracy of 22%.

The first issue to emerge was noise, with the team determining that the greyscale value of noise and text was always within two distinct and constant ranges. Tweaks to the tool helped filter out the noise.

The team also realized that some of the test CAPTCHAs had been labelled incorrectly, with confusion between, for example, l and I (lower case L and upper case i). Fixing this shortcoming brought the accuracy up to 47%.

More challenging, though, was handling the CAPTCHA submission to Outlooks web portal.

There was no CAPTCHA POST request, with the CAPTCHA instead sent as a value appended to a cookie. JavaScript was used to keylog the user as the answer to the CAPTCHA was typed.

Instead of trying to replicate what occurred in JS, we decided to use Pyppeteer, a browsing simulation Python package, to simulate a user entering the CAPTCHA, said Tinus Green, a senior information security consultant at F-Secure

Doing this, the JS would automatically take care of the submission for us.

Green added: We could use this simulation software to solve the CAPTCHA whenever it blocked entries and once solved, we could continue with our conventional attack, hence automating the process once again.

We have now also refactored CAPTCHA22 for a public release.

CAPTCHAs are challenge-response tests used by many websites in an attempt to distinguish between genuine requests to sign-up to or access web services by a human user and automated requests by bots.

Spammers, for example, attempt to circumvent CAPTCHAs in order to create accounts they can later abuse to distribute junk mail.

CAPTCHAs are something of a magnet for cybercriminals and security researchers, with web admins struggling to stay one step ahead.

Late last year, for example, PortSwigger Web Security uncovered a security weakness in Googles reCAPTCHA that allowed it to be partially bypassed by using Turbo Intruder, a research-focused Burp Suite extension, to trigger a race condition.

Soon after, a team of academics from the University of Maryland was able to circumvent Googles reCAPTCHA v2s anti-bot mechanism using a Python-based program called UnCaptcha, which could solve its audio challenges.

Green said: There is a catch 22 between creating a CAPTCHA that is user friendly grandma safe as we call it and sufficiently complex to prevent solving through computers. At this point it seems as if the balance does not exist.

Web admins shouldnt, he says, give away half the required information through username enumeration, and users should be required to set strong pass phrases conforming to NIST standards.

And, he adds: Accept that accounts can be breached, and therefore implement MFA [multi-factor authentication] as an additional barrier.

RELATED New tool highlights shortcomings in reCAPTCHAs anti-bot engine

Read more here:
Machine learning techniques applied to crack CAPTCHAs - The Daily Swig

As many companies struggle in the midst of the COVID-19 pandemic, Tecton.ai has managed to garner a $20-million investment fromAndreessen Horowitz and Sequoia Capitalin April 2020.

Tecton.ai was founded by members who created Uber Inc.s Michelangelo, an end-to-end workflow that enablesinternal teamsto seamlessly build, deploy and operate machine-learning solutionsatscale. Through the lessons learned at Uber, the founders of Tecton branched out to create a world-class data platform for machine learning accessible to every company.

So why did this appeal so much to investorslike Andreessen Horowitz? Because while data is the future, wrangling data is still one of the most complex tasks that organizations and data scientists can do. And tools that incorporate machine learning must continue to be developed in order to help enterprises understand the overwhelmingly vast world of data.

I actually think this is probably the biggest shift certainly Ive seen in my career, saidMartin Casado(pictured, left), general partner at Andreessen Horowitz. It used to be if you looked at a system you wrote bad code, you made bugs, you had vulnerabilities in your code that would dictate the system. But more and more, thats actually not the case. You create these models, you feed the data models, the data gives you output, and your workflows around those models are really dictating things.

CasadoandMike Del Balso(pictured, right), co-founder and chief executive officer of Tecton, spoke with Stu Miniman,host of theCUBE, SiliconANGLE Medias livestreaming studio,during a digital CUBE Conversation. They discussed Tectons future, machine learning, and the importance of the data industry.(* Disclosure below.)

The importance of data cant be overstated, according to Casado. I honestly think the data industry is going to be 10 times the computer industry, he said. With compute, youre building houses from the ground up, and theres a ton of value there. With data youre extracting insight and value from the universe, right? Its like the natural system.

In 2020, 90% of business professionals and enterprise analytics say data and analytics are key to their organizations digital transformation initiatives, according to a recent study by Acute Market Reports. Both Casado and Del Balso believe that Tecton has a chance to be a very pivotal company in democratizing access to data. The opportunity is enormous because data is still hard to capture, clean up, and interpret in effective ways. In fact, almost three-quarters (73.5%) of recent survey respondents said they spend 25% or more of their time managing, cleaning, and/or labeling data, according to an Appen Ltd.whitepaper.Andthe demand for data scientists increased32% in 2019 compared to the previous year, according to aDice Tech Jobsreport released in February.

What we dont really know is, how do you take data and reign it in so you can use it in the same way that you use software system? Casado stated. Talking about things like data network effects and extracting data is a little bit preliminary, because we still actually dont even understand how much work it takes to mine insights from data. So I think that were now in this era building the tooling that is required to extract the insights of that data. And I think thats a very necessary step, and this is where a Tecton comes in to provide that tooling.

Tecton is a data platform for machine learning that manages all the feature data and transformations to allow an organization to share predictive signals across use cases and understand what they are, according to Del Balso. During their time with Uber, Del Balsoand the other founders of Tectonrecognized that a feature management layer was the component that really allows a company to scale out machine learning across a number of different use cases, and allows individual data scientists to own more than just one model in production.

In a machine-learning application, theres fundamentally two components, right? Theres a model that you have to build thats going to make the decisions given a certain set of inputs, and then theres the features, which end up being those inputs that the model uses to make the decision, Del Balso explained. And common machine-learning infrastructure stats really are split into two layers. Theres a model management layer and a feature management layer, and thats an emerging pattern in some of the more sophisticated machine-learning stacks that are out there.

At the core of Tectons strategyare a few simple components. The first is feature pipelines, which are data pipelines that plug into a business raw data and turn them into features with predictive signals. The second part of that is a feature store, which catalogs these pipelines and draws the output feature data. The third component is feature service and making data accessible to a data scientist when theyre building their models so they can make these decisions, which is sometimes needed in milliseconds for real-time decisioning.

Were at private beta with a number of customers, Del Balso said. We are spending time engaging in deep, hands-on engagements with different teams who are really trying to set up their machine learning on the cloud, figuring out how to get their machine learning in production. And it tends to be teams that are trying to really use machine learning for operational use cases really trying to drive real business decisions and power their product customer experiences.

Watch the complete video interview below, and be sure to check out more of SiliconANGLEs and theCUBEsCUBE Conversations.

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Data, not code, will dictate systems of the future, says Tecton.ai - SiliconANGLE News

TEL AVIV, Israel, May 26, 2020 /PRNewswire/ --cnvrg.io, the data science platform simplifying model management and introducing advanced MLOps to the industry, today announced its streaming endpoints solution, a new capability for deploying ML models to production with Apache Kafka in one click. cnvrg.iois the first ML platform to enable one click streaming endpoint deployment for large-scale and real-time predictions with high throughput and low latency.

85% of machine learning models don't get to production due to the technical complexity of deploying the model in the right environment and architecture. Models can be deployed in a variety of different ways. Batch deployment for offline inference and web service for more real-time scenarios. These two approaches cover most of the ML use cases, but they both fall short in an enterprise setting when you need to scale and stream millions of predictions in real time. Enterprises require fast, scalable predictions to execute critical and time sensitive business decisions.

cnvrg.iois thrilled to announce its new capability of deploying ML models to production with a streaming architecture of producer/consumer interface with native integration to Apache Kafka and AWS Kinesis. In just one click, data scientists and engineers can deploy any kind of model as an endpoint that can receive data as stream and output predictions as streams.

Deployed models will be tracked with advanced model management and model monitoring solutions including alerts, retraining, A/B testing and canary rollout, autoscaling and more.

This new capability allows engineers to support and predict millions of samples in a real-time environment. This architecture is ideal for time sensitive or event-based predictions, recommender systems, and large-scale applications that require high throughput, low latency and fault tolerant environments.

"Playtika has 10 million daily active users (DAU), 10 billion daily events and over 9TB of daily processed data for our online games. To provide our players with a personalized experience, we need to ensure our models run at peak performance at all times," says Avi Gabay, Director of Architecture at Playtika. "With cnvrg.io we were able to increase our model throughput by up to 50% and on average by 30% when comparing to RESTful APIs. cnvrg.io also allows us to monitor our models in production, set alerts and retrain with high-level automation ML pipelines."

The new cnvrg.io release extends the market footprint and enhances the prior announcements of NVIDIA DGX-Ready partnership and Red Hat unified control plane.

About cnvrg.io

cnvrg.io is an AI OS, transforming the way enterprises manage, scale and accelerate AI and data science development from research to production. The code-first platform is built by data scientists, for data scientists and offers unrivaled flexibility to run on-premise or cloud.

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Full Stack Machine Learning Operating System

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Complex machines that steer autonomous vehicles, set the temperature in our homes and buy and sell stocks with little human control are built to learn from their environments and act on what they see or hear. They can be tricked into grave errors by relatively simple attacks or innocent misunderstandings, but they may be able to help themselves by mixing their senses.

In 2018, a group of security researchers managed to befuddle object-detecting software with tactics that appear so innocuous its hard to think of them as attacks. By adding a few carefully designed stickers to stop signs, the researchers fooled the sort of object-recognizing computer that helps guide driverless cars. The computers saw an umbrella, bottle or banana but no stop sign.

Two multi-colored stickers attached to a stop sign were enough to disguise it to the eyes of an image-recognition algorithm as a bottle, banana and umbrella. UW-Madison

They did this attack physically added some clever graffiti to a stop sign, so it looks like some person just wrote on it or something and then the object detectors would start seeing it is a speed limit sign, says Somesh Jha, a University of WisconsinMadison computer sciences professor and computer security expert. You can imagine that if this kind of thing happened in the wild, to an auto-driving vehicle, that could be really catastrophic.

The Defense Advanced Research Projects Agency has awarded a team of researchers led by Jha a $2.7 million grant to design algorithms that can protect themselves against potentially dangerous deception. Joining Jha as co-investigators are UWMadison Electrical and Computer Engineering Professor Kassem Fawaz, University of Toronto Computer Sciences Professor Nicolas Papernot, and Atul Prakash, a University of Michigan professor of Electrical Engineering and Computer Science and an author of the 2018 study.

Kassem Fawaz

One of Prakashs stop signs, now an exhibit at the Science Museum of London, is adorned with just two narrow bands of disorganized-looking blobs of color. Subtle changes can make a big difference to object- or audio-recognition algorithms that fly drones or make smart speakers work, because they are looking for subtle cues in the first place, Jha says.

The systems are often self-taught through a process called machine learning. Instead of being programmed into rigid recognition of a stop sign as a red octagon with specific, blocky white lettering, machine learning algorithms build their own rules by picking distinctive similarities from images that the system may know only to contain or not contain stop signs.

The more examples it learns from, the more angles and conditions it is exposed to, the more flexible it can be in making identifications, Jha says. The better it should be at operating in the real world.

But a clever person with a good idea of how the algorithm digests its inputs might be able to exploit those rules to confuse the system.

DARPA likes to stay a couple steps ahead, says Jha. These sorts of attacks are largely theoretical now, based on security research, and wed like them to stay that way.

A military adversary, however or some other organization that sees advantage in it could devise these attacks to waylay sensor-dependent drones or even trick largely automated commodity-trading computers run into bad buying and selling patterns.

Somesh Jha

What you can do to defend against this is something more fundamental during the training of the machine learning algorithms to make them more robust against lots of different types of attacks, says Jha.

One approach is to make the algorithms multi-modal. Instead of a self-driving car relying solely on object-recognition to identify a stop sign, it can use other sensors to cross-check results. Self-driving cars or automated drones have cameras, but often also GPS devices for location and laser-scanning LIDAR to map changing terrain.

So, while the camera may be saying, Hey this is a 45-mile-per-hour speed limit sign, the LIDAR says, But wait, its an octagon. Thats not the shape of a speed limit sign, Jha says. The GPS might say, But were at the intersection of two major roads here, that would be a better place for a stop sign than a speed limit sign.

The trick is not to over-train, constraining the algorithm too much.

The important consideration is how you balance accuracy against robustness against attacks, says Jha. I can have a very robust algorithm that says every object is a cat. It would be hard to attack. But it would also be hard to find a use for that.

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Teaching machine learning to check senses may avoid sophisticated attacks - University of Wisconsin-Madison

CMI announced that its published an exclusive report namelyGlobal Machine Learning as a Service (MLaaS) Marketby Manufacturers, Regions, Type and Application, Forecast to 2027 in its research database with report summary, table of content, research methodologies and data sources. The research study offers a substantial knowledge platform for entrants and investors as well as veteran companies, manufacturers functioning in the WorldwideMachine Learning as a Service (MLaaS)Market. This is an informative study covering the market with in-depth analysis and portraying the current state of affairs in the industry.

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Machine Learning as a Service (MLaaS) Market Down To A Trickle Month Other Covid-19 Traders Cling On The Hope. - Cole of Duty

The next progression in organizations using threat intelligence is adding AI threat intelligence capabilities, in the form of machine learning technologies, to improve attack detection. Machine learning is a form of AI that enables computers to analyze data and learn its significance. The rationale for using machine learning with threat intelligence is to enable computers to more rapidly detect attacks than humans can and stop those attacks before more damage occurs. In addition, because the volume of threat intelligence is often so large, traditional detection technologies inevitably generate too many false positives. Machine learning can analyze the threat intelligence and condense it into a smaller set of things to look for, thereby reducing the number of false positives.

This sounds fantastic, but there's a catch -- actually, a few catches. Expecting AI to magically improve security is unrealistic, and deploying machine learning without preparation and ongoing support may make things worse.

Here are three steps enterprises should take to use AI threat intelligence tools with machine learning capabilities to improve attack detection.

AI threat intelligence products that use machine learning work by taking inputs, analyzing them and producing outputs. For attack detection, machine learning's inputs include threat intelligence, and its outputs are either alerts indicating attacks or automated actions stopping attacks. If the threat intelligence has errors, it will give "bad" information to the attack detection tools, so the tools' machine learning algorithms may produce "bad" outputs.

Many organizations subscribe to multiple sources of threat intelligence. These include feeds, which contain machine-readable signs of attacks, like the IP addresses of computers issuing attacks and the file names used by malware. Other sources of threat intelligence are services, which generally provide human-readable prose describing the newest threats. Machine learning can use feeds but not services.

Organizations should use the highest quality threat intelligence feeds for machine learning. Characteristics to consider include the following:

It's hard to directly evaluate the quality of threat intelligence, but you can indirectly evaluate it based on the number of false positives that occur from using it. High-quality threat intelligence should lead to minimal false positives when it's used directly by detection tools -- without machine learning.

False positives are a real concern if you're using threat intelligence with machine learning to do things like automatically block attacks. Mistakes will disrupt benign activity and could negatively affect operations.

Ultimately, threat intelligence is just one part of assessing risk. Another part is understanding context -- like the role, importance and operational characteristics of each computer. Providing contextual information to machine learning can help it get more value from threat intelligence. Suppose threat intelligence indicates a particular external IP address is malicious. Detecting outgoing network traffic from an internal database server to that address might merit a different action than outgoing network traffic to the same address from a server that sends files to subscribers every day.

The toughest part of using machine learning is providing the actual learning. Machine learning needs to be told what's good and what's bad, as well as when it makes mistakes so it can learn from them. This requires frequent attention from skilled humans. A common way of teaching machine learning-enabled technologies is to put them into a monitor-only mode where they identify what's malicious but don't block anything. Humans review the machine learning tool's alerts and validate them, letting it know which were erroneous. Without feedback from humans, machine learning can't improve on its mistakes.

Conventional wisdom is to avoid relying on AI threat intelligence that uses machine learning to detect attacks because of concern over false positives. That makes sense in some environments, but not in others. Older detection techniques are more likely to miss the latest attacks, which may not follow the patterns those techniques typically look for. Machine learning can help security teams find the latest attacks, but with potentially higher false positive rates. If missing attacks is a greater concern than the resources needed to investigate additional false positives, then more reliance on automation utilizing machine learning may make sense to protect those assets.

Many organizations will find it best to use threat intelligence without machine learning for some purposes, and to get machine learning-generated insights for other purposes. For example, threat hunters might use machine learning to get suggestions of things to investigate that would have been impossible for them to find in large threat intelligence data sets. Also, don't forget about threat intelligence services -- their reports can provide invaluable insights for threat hunters on the newest threats. These insights often include things that can't easily be automated into something machine learning can process.

More here:
AI threat intelligence is the future, and the future is now - TechTarget

Machine-learning algorithms are responsible for the vast majority of the artificial intelligence advancements and applications you hear about. (For more background, check out our first flowchart on "What is AI?" here.)

Machine-learning algorithms use statistics to find patterns in massive* amounts of data. And data, here, encompasses a lot of thingsnumbers, words, images, clicks, what have you. If it can be digitally stored, it can be fed into a machine-learning algorithm.

Machine learning is the process that powers many of the services we use todayrecommendation systems like thoseon Netflix, YouTube, and Spotify; search engines like Google and Baidu; social-media feeds like Facebook and Twitter; voice assistants like Siri and Alexa. The list goes on.

In all of these instances, each platform is collecting as much data about you as possiblewhat genres you like watching, what links you are clicking, which statuses you are reacting toand using machine learning to make a highly educated guess about what you might want next. Or, in the case of a voice assistant, about which words match best with the funny sounds coming out of your mouth.

Frankly, this process is quite basic: find the pattern, apply the pattern. But it pretty much runs the world. Thats in big part thanks to an invention in 1986, courtesy of Geoffrey Hinton, today known as the father of deep learning.

Deep learning is machine learning on steroids: it uses a technique that gives machines an enhanced ability to findand amplifyeven the smallest patterns. This technique is called a deep neural networkdeep because it has many, many layers of simple computational nodes that work together to munch through data and deliver a final resultin the form of the prediction.

Neural networks were vaguely inspired by the inner workings of the human brain. The nodes are sort of like neurons, and the network is sort of like the brain itself. (For the researchers among you who are cringing at this comparison: Stop pooh-poohing the analogy. Its a good analogy.) But Hinton published his breakthrough paper at a time when neural nets had fallen out of fashion. No one really knew how to train them, so they werent producing good results. It took nearly 30 years for the technique to make a comeback. And boy, did it make a comeback.

One last thing you need to know: machine (and deep) learning comes in three flavors: supervised, unsupervised, and reinforcement. In supervised learning, the most prevalent, the data is labeled to tell the machine exactly what patterns it should look for. Think of it as something like a sniffer dog that will hunt down targets once it knows the scent its after. Thats what youre doing when you press play on a Netflix showyoure telling the algorithm to find similar shows.

In unsupervised learning, the data has no labels. The machine just looks for whatever patterns it can find. This is like letting a dog smell tons of different objects and sorting them into groups with similar smells. Unsupervised techniques arent as popular because they have less obvious applications. Interestingly, they have gained traction incybersecurity.

Lastly, we have reinforcement learning, the latest frontier of machine learning. A reinforcement algorithm learns by trial and error to achieve a clear objective. It tries out lots of different things and is rewarded or penalized depending on whether its behaviors help or hinder it from reaching its objective. This is like giving and withholding treats when teaching a dog a new trick. Reinforcement learning is the basis of Googles AlphaGo, the program that famously beat the best human players in the complex game of Go.

Thats it. That's machine learning. Now check out the flowchart above for a final recap.

*Note: Okay, there are technically ways to perform machine learning on smallish amounts of data, but you typically need huge piles of it to achieve good results.

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This originally appeared in our AI newsletter The Algorithm. To have it directly delivered to your inbox,subscribe herefor free.

Read more here:
What is machine learning? | MIT Technology Review