Daily Archives: December 13, 2019

Qualitest, the worlds largest software testing and quality assurance company, has acquired AI and machine learning company AlgoTrace for an undisclosed amount. This acquisition marks the first step of Qualitests growth strategy following an investment from Bridgepoint earlier this year.

The acquisition will allow Qualitest to radically expand the number of AI-powered testing solutions available to clients, as well as develop its capabilities in assisting companies test and launch new AI-powered solutions with greater confidence and speed. As software grows in complexity and the pressure to launch faster and more frequently increases, according to Gartner, companies that do not use AI to enhance their Quality Assurance will be at a significant disadvantage.

AlgoTraces machine learning tools help brands answer business critical questions as they launch new software: what, where, when, and how to test and in what order to ensure consistently high quality. With multiple clients already using Qualitests suite of AI-testing tools, this expansion of capabilities creates opportunities not only for new Qualitest clients, but also allows for the growth of existing relationships with current customers around the world.

Read More: Higher Adoption of Emerging Technologies in Commercial Vehicles Stoke OEM Collaborations with

Qualitest began working with the AlgoTrace team more than a year ago, with AlgoTraces AI platform powering Qualitests market-leading test predictor tool, which applies pioneering autonomous AI capabilities and predictive modeling to unstructured data without the need for code or complex interfaces. Following multiple successful joint projects, the teams saw that, together, they would be able to apply AlgoTraces powerful prediction engine in a variety of ways across the software development lifecycle to improve quality and speed to market.

Qualitests AI-testing solutions have two main features focused on increasing confidence and assurance. First, to assist and enhance quality assurance efforts giving brands, in a more rapid fashion, high levels of confidence that software releases will go smoothly. Second, helping companies who are using AI in their own offerings to have a higher level of confidence that their AI algorithms are generating correct, unbiased results.

Ron Ritter, CEO at AlgoTrace, said: We are thrilled to be joining with Qualitest. Following successful implementations with the company in the past, we have complete faith that we will help Qualitest change the testing paradigm forever enhancing their quality engineering with machine learning. While there is a lot of hype surrounding AI, were deploying real, hard-nosed and practical tools that significantly change the rules.

Read More: AiThority Interview with Jason Braverman, Chief Technology Officer at SkyX Systems Corp.

Norm Merritt, CEO of Qualitest, said:Applying AI to quality engineering is a perfect fit. Just as software becomes increasingly complex, the companies producing it are under competitive pressure to increase the speed and frequency of their rollouts. AI is the only way companies can scale software testing and quality engineering and the AlgoTrace team have shown that they understand this. In our view, companies that do not use AI to improve quality will be at a significant disadvantage.

Aviram Shotten, Chief Knowledge and Innovation Officer at Qualitest, said: Ron and his team are just the kind of innovators we love: smart, customer-obsessed and attacking a big market problem with cutting edge technology. This acquisition will not only help us accelerate AI adoption within quality engineering by providing a holistic solution to our clients, it provides an avenue for our teams to access AlgoTraces unique expertise to build new models, tools and solutions to improve how technology is developed, tested and deployed.

Read More: ImmersiveTouch Launches New Personalized VR Imaging Platform into the Radiology Market

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Qualitest Acquires AI and ML Company AlgoTrace to Expand Its Offering - AiThority

By Julien Legrand, Operation Security Manager, Socit Gnrale

Artificial intelligence is a double-edged sword that can be used as a security solution or as a weapon by hackers. AI entails developing programs and systems capable of exhibiting traits associated with human behaviors. The characteristics include the ability to adapt to a particular environment or to intelligently respond to a situation. AI technologies have extensively been applied in cybersecurity solutions, but hackers are also leveraging them to develop intelligent malware programs and execute stealth attacks.

Security experts have conducted a lot of research to harness the capabilities of AI and incorporate it into security solutions. AI-enabled security tools and products can detect and respond to cybersecurity incidents with minimal or zero input from humans. AI applications in cybersecurity have proved to be highly useful. Twenty-five percent of IT decision-makers attribute security as the primary reason why they adopt AI and machine learning in organizational cybersecurity. AI not only improves security posture, but it also automates detection and response processes. This cuts on the finances and time used in human-driven intervention and detection processes.

Organizations use AI to model and monitor the behavior of system users. The purpose of monitoring the interactions between a system and users is to identify takeover attacks. These are attacks where malicious employees steal login details of other users and use their accounts to commit different types of cybercrimes. AI learns the user activities over time such that it considers unusual behavior as anomalies. Whenever a different user uses the account, AI-powered systems can detect the unusual activity patterns and respond either by locking out the user or immediately alert system admins of the changes.

Antivirus tools with AI capabilities detect network or system anomalies by identifying programs exhibiting unusual behavior. Malware programs are coded to execute functions that differ from standard computer operations. AI antiviruses leverage machine learning tactics to learn how legitimate programs interact with an operating system. As such, whenever malware programs are introduced to a network, AI antivirus solutions can immediately detect them and block them from accessing systems resources. This contrasts from signature-based traditional antiviruses which scans a signature database to determine whether a program is a security threat.

Automated analysis of system or network data ensures continuous monitoring for prompt identification of attempted intrusions. Manual analysis is nearly impossible due to the sheer volume of data generated by user activities. Cybercriminals use command and control (C2) tactics to penetrate network defenses without being detected. Such tactics include embedding data in DNS requests to bypass firewalls and IDS/IPS. AI-enabled cyber defenses utilize anomaly detection, keyword matching, and monitoring statistics. As a result, they can detect all types of network or system intrusion.

Cybercriminals prefer email communication as the primary delivery technique for malicious links and attachments used to conduct phishing attacks. Symantec states that 54.6 percent of received email messages are spam and may contain malicious attachments or links. Anti-phishing emails with AI and machine learning capabilities are highly effective in identifying phishing emails. This is by performing in-depth inspections on links. Additionally, such anti-phishing tools simulate clicks on sent links to detect phishing signs. They also apply anomaly detection techniques to identify suspicious activities in all features of the sender. These include attachments, links, message bodies, among other items.

Hackers are turning to AI and using it to weaponize malware and attacks to counter the advancements made in cybersecurity solutions. For instance, criminals use AI to conceal malicious codes in benign applications.They program the codes to execute at a specific time, say ten months after the applications have been installed, or when a targeted number of users have subscribed to the applications. This is to maximize the impacts such attacks will cause. Concealing such codes and information requires the application of AI models and deriving private keys to control the place and time the malware will execute.

Notwithstanding, hackers can predefine an application feature as an AI trigger for executing cyber-attacks. The features can range from authenticating processes through voice or visual recognition to identity management features. Most applications used today contain such features, and this provides attackers with ample opportunities of feeding weaponized AI models, deriving a key, and attacking at will. The malicious models can be present for years without detection as hackers wait to strike when applications are most vulnerable.

Besides, AI technologies are unique in that they acquire knowledge and intelligence to adapt accordingly. Hackers are aware of these capabilities and leverage them to model adaptable attacks and create intelligent malware programs. Therefore, during attacks, the programs can collect knowledge of what prevented the attacks from being successful and retain what proved to be useful. AI-based attacks may not succeed in a first attempt, but adaptability abilities can enable hackers to succeed in subsequent attacks. Security communities thus need to gain in-depth knowledge of the techniques used to develop AI-powered attacks to create effective mitigations and controls.

Also, cyber adversaries use AI to execute intelligent attacks that self-propagate over a system or network. Smart malware can exploit unmitigated vulnerabilities leading to an increased likelihood of fully compromised targets. If an intelligent attack comes across a patched vulnerability, it immediately adapts to try compromising a system through different types of attacks.

Lastly, hackers use AI technologies to create malware capable of mimicking trusted system components. This is to improve stealth attacks. For example, cyber actors use AI-enabled malware programs to automatically learn the computation environment of an organization, patch update lifecycle, preferred communication protocols, and when the systems are least protected. Subsequently, hackers can execute undetectable attacks as they blend with an organizations security environment. For example, TaskRabbit was hacked compromising 3.75 million users, yet investigations could not trace the attack. Stealth attacks are dangerous since hackers can penetrate and leave a system at will. AI facilitates such attacks, and the technology will only lead to the creation of faster and more intelligent attacks.

Disclaimer: CISO MAG does not endorse any of the claims made by the writer. The facts, opinions, and language in the article do not reflect the views of CISO MAG and CISO MAG does not assume any responsibility or liability for the same. Views expressed in this article are personal.

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Artificial Intelligence as Security Solution and Weaponization by Hackers - CISO MAG

Decision algorithms are commonly infected with biases

Noel Sharkey, an expert in the field of artificial intelligence (AI), has urged the government to ban the use of all decision algorithms that impact on peoples' lives.

In an interview with theGuardian, Prof Sharkey said that such algorithms are commonly "infected with biases," and one should not expect them to make fair or trusted decisions.

"There are so many biases happening now, from job interviews to welfare to determining who should get bail and who should go to jail. It is quite clear that we really have to stop using decision algorithms," says the Sheffield University professor and robotics/AI pioneer, who is also a leading figure in a campaign against autonomous weapons.

"I am someone who has always been very light on regulation and always believed that it stifles innovation. But then I realised eventually that some innovations are well worth stifling, or at least holding back a bit. So I have come down on the side of strict regulation of all decision algorithms, which should stop immediately," he added.

According to Sharkey, all leading tech firms, such as Microsoft and Google, are aware of the algorithm bias problem and are also working on it, but none of them has so far come up with a solution.

Sharkey believes AI decision-making machines needs to be tested in the same way as any new pharmaceutical drug is before it is allowed on to the market.

By this, Sharkey means testing AI systems on at least hundreds of thousands, and preferably millions, of people, to eventually reach a point where the algorithm shows no major inbuilt bias.

To address this issue, US lawmakers introduced the Algorithmic Accountability Act in the lower House and Senate this April. The Act would require technology companies to ensure that their machine learning algorithms arefree of gender, race, and other biases before deployment.

If passed, the bill would require the Federal Trade Commission to create guidelines for assessing 'highly sensitive' automated systems. If companies find an algorithm implying the risk of privacy loss, they would take corrective actions to fix everything that is "inaccurate, unfair, biased or discriminatory" in the algorithm.

Last month Arvind Narayanan, an associate professor at Princeton, warned that most of the products or applications being sold today as AI are little more than "snake oil".

According to Narayanan, many companies have been using AI-based assessment systems to screen applicants. The majority of such systems claim to work not by analysing what the candidate said or wrote in their CV, but by speech pattern and body language.

"Common sense tells you this isn't possible, and AI experts would agree," Narayanan said.

According to Narayanan, the areas where AI use will remain fundamentally dubious include predicting criminal recidivism, forecasting job performance and predictive policing.

Arecent study by Oxford Internet Institute researchers suggested that current laws have largely failed to protect the public from biased algorithms that influence decision-making on everything from housing to employment.

The lead researcher of the study found many algorithms that were making conclusions about personal traits such as gender, ethnicity, religious beliefs and sexual orientation based on the individuals' browsing behaviour.

More here:

AI expert calls for end to use of 'racially biased' algorithms - http://www.computing.co.uk

1. Facial recognition and AI in video interviewsA number of video interview platforms available on the market today focus on scheduling a Q&A with a candidate, recording a clip of the exchange, and forwarding it to the HR team for assessment.

Recruitment tech specialist HireVue, however, takes candidate screening to the next level by combining facial analysis with AI.

The software behind the platform purportedly relies on 25,000 data points taken from the facial expressions, movements and tone of voice of past successful candidates then uses them as a benchmark for screening new applicants. These subtle clues from their interaction with the AI reportedly help determine their suitability to the role.

2. Reducing unconscious bias in candidate interviewsAmid efforts to build a more diverse workforce, organizations still struggle against the influence of unconscious bias in hiring. Stockholm-based tech firm Furhat Robotics, however, is working to reduce the impact of human bias on candidate screening all with the help of the robotic interviewer Tengai.

Unlike human recruiters, the robot skips the small talk and goes through the questions in the same manner, tone and order. The developers hope this approach leads to a fairer recruitment process.

Read more:

Five ways AI changed the workplace in 2019 - Human Resources Director

Yona. (Courtesy of Auxuman)

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A mechanical way of thinking, an artificial kind of intelligence, has underlaid the making of popular music since the invention of the form. Thats why we can talk about pop as an invention with a given form. As early as the days of hit songs written on sheet music, before the advent of recordings and radio, Charles K. Harris, writer of the first million-selling song sheet, After the Ball, described the trade of tunesmithing in industrial terms: a matter of applying procedures derived from past successes, calibrating them for mass consumption. He wrote an instruction manual, How to Write a Popular Song, in 1906, with a checklist of essential rules. In his words:Ad Policy

Watch your competitors. Note their success or failure; analyze the cause and profit thereby.

Note public demand.

If you do not feel confident to write or compose a certain style of songadapt yourself to the others.

Over more than a century since Harriss era, the proposition that songs should be made according to rules based on precedent and shaped by market forces is taken as a given and, indeed, valued as a way of honoring tradition and pleasing the public in many spheres of music, from country and gospel to jazz and R&B. At the same time, talk of regimented production or subordination of the creative impulse is widely and freely employed as criticism. Music journalists and critics have few tools as piercing as the charge that a song is formulaic or mechanical, or that an artist is pandering or a sellout.

The growing use of artificial intelligence in music challenges us to consider not only songwriting but also singing and musicianship in ways that are essentially extensions of the machine-age thinking of Harris and, at the same time, startlingly new. A small but expanding group of tech innovators has been developing a range of musical applications of AI such as Boomy, which, so far, has allowed people to make more than 400,000 songs through a combination of machine learning and input from human users. Earlier this year, the start-up Endel released a series of albums of AI-generated ambient music on the major streaming services, through a partnership with Warner Music. The company is planning to release 20 albums by the end of this year, all in the vein of chill playlists, with candle-scent-like titles that signal their purpose of conjuring soothing atmospheres: Clear Night, Rainy Night, Cloudy Afternoon, Cloudy Night, and Foggy Morning. Wordless, nearly formless, and harmless, theyre perfectly functional background music, well-marketed to make an asset of the absence of anything warranting the listeners attention. A more recent project from another start-up, Auxuman, is an achievement on another level and may well be a watershed in pop music history.

Auxuman (brand shorthand for auxiliary human) is the brainchild of the British Iranian interdisciplinary artist Ash Koosha (aka Ashkan Kooshenaejad). He first established himself with a couple of pleasantly atmospheric synth-based pop albums, the first of which, Guud, included a single, I Feel That, whose official video starred a synthetic semihumanoid, created by the digital artist Hirad Sab. Before turning to AI, Koosha made some multisensory art for VR headsets. Now Koosha and a team of programmers have created a stable of digital music acts who (Ill use the personal pronoun for human beings in deference to the uman in Auxuman) will be releasing a full album of new material every month under the umbrella name Auxuman.

As Koosha has described the Auxuman process to the tech-fan site Digital Trends, the words, music, instruments, and singing voices on the tracks are generated by mining existing music on the Web and processing it to generate new songs. The synthetic artists who are the public face of the work are, in Kooshas words, a reflection of human life on the internet. Their music comes from stories we have told, ideas we have generated, and opinions we have shared. The principles are not far removed from Harriss rules for analyzing the music of other songwriters and factoring in public demand.

There are five digital performers in the Auxuman collective to date: Gemini, Hexe, Mony, Yona, and Zoya. Visually, in the avatar imagery that accompanies each song on YouTube, theyre a mix of racial and ethnic identities, in a few cases thoroughly and indefinitely mixed. Four (Gemini, Hexe, Mony, and Yona) are distinctly female in appearance, one (Zoya) male. The one front and center in group pictures, Yona, is, a pixieish white woman who looks like what she is: a computers idea of a pop star.

In September, the first music attributed to the five was released as a 10-track album, Auxuman #1. Taking in these recordings, at first I tried to shake the fact that they were generated by AI, and sought to listen with no preconceptions or expectations. Within a minute, I realized that was pointless and unfair to the work and its digital creators. With Auxuman #1, we are forced to confront a genuinely new type of music that works on its own terms.

All five voices, though somewhat distinct from one another, sound of a piece and appropriately artificialmetallic in tone and rigidly staccato in their diction and phrasing. The voices carry no warmth and have no flexibility. They sound inhuman, soulless, but fascinatingly so. To expect otherwise from them would be as wrongheaded as it was for early listeners of recordings to expect pioneers of the microphone such as Billie Holiday and Frank Sinatra to belt like Al Jolson, Sophie Tucker, and others who needed to bellow to reach the rafters of big concert halls. Yona, in particular, exudes impersonal detachment and superficiality. As well as any other artist I can think of, she gives voice to the sense of isolation that chills the air of the digital world.

The lyrics are an eerie jumble of phrases, mostly trite babbling, not unlike the words of a fair number of pop songs since Charles K. Harriss day. From time to time, though, the random juxtapositions come together in unnerving, accidental eloquence. In Oblivious, Yona sings:

Ive never felt warmIve never felt warmThrough the lensThrough your lensI feel warm

We know she cannot feel anything. And she communicates only coldness. Knowing this, as I listened, I found myself projecting onto her and started to feel bad for her. Through my lens, I gave her warmth.

The music on all 10 tracks is perfectly, unsettlingly synthetic. Every note is placed precisely on beat. The harmony in every chord is correct, technically. And yet, nothing sounds quite right. Theres something profoundly but fittingly disturbing about it all. Its utterly conventional and predictable in its formal structures and musical particulars, but wholly arbitrary, built of nothing but its own surface qualities. It sounds like what it is: code pretending to be life. I can think of nothing more relevant.

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Do Auxuman's AI Singers Herald the Shape of Music to Come? - The Nation

When it recently launched a new weather model called IBM GRAF, The Weather Company took a big supercomputing step forward.

IBM GRAF, with its ability to process weather data from a variety of sources worldwide, enables The Weather Company, an IBM Business, to deliver high-resolution, hourly-updated forecasts around the globeparticularly to regions that have never had them before.

But the full power of IBM GRAF to generate local forecasts for the entire world depends on technology that The Weather Company already had in place and is continually refining: artificial intelligence (AI) and cloud computing.

When a series of winter storms recently lashed much of the United States, millions of people used The Weather Channel mobile app and weather.com website to help plan their travel.

Sophisticated AI algorithms from The Weather Company turn troves of current and historic weather data into recommendations, for example, that can tell an electric utility company where to trim trees to prevent blackouts before the next storm hits. Or that can compute a two-week flu-risk forecast for a given locale.

And it is the mix of IBM Cloud and hybrid cloud networks that can deliver The Weather Companys forecasts anywhere in the worldto business customers computer systems and for free to millions of users via smartphones and web apps.

You have to know whats happening everywhere in the atmosphere, right now, said Cameron Clayton, general manager of IBM Watson Media and Weather. Supercomputing and AI have had a profound impact on our ability to map the atmosphere and predict the future. The cloud helps us share those forecasts any time, anywhere.

IBM GRAFshorthand for Global High-Resolution Atmospheric Forecasting systemruns on a purpose-built IBM POWER9 supercomputer known as Dyeus. It maps conditions at billions of points in the atmosphere to produce hyperlocal pictures of what the weather will look like up to 12 hours in advance. It also produces a fresh set of predictions every hour, rather than every 6 or 12, as with many weather models in other parts of the world.

But thats just the beginning. Those predictions feed into the multi-model forecasting engine that The Weather Company, part of IBM, has been refining for more than two decades. Using complex machine learning algorithms, that engine combines the IBM GRAF results with those of about 100 other weather forecasts from around the world, including the American modelthe Global Forecast System used by the National Weather Serviceand the European model, ECMWF.

All these other models have different opinions about what the forecast is, said Peter Neilley, an IBM distinguished engineer and director of weather forecasting sciences and technologies for The Weather Company. The AI or machine learning is the thing that binds them all together and figures out the right way to combine them to produce an optimized forecast.

To do that, the engine compares factors like temperature or precipitation from each model based on geography, time, weather type and recent forecast accuracy and assigns them relative weights and correction factors. The system then blends those weighted contributions to arrive at the final forecast. The calculations involve some 400 terabytes of data collected daily to provide forecasts for two billion locations around the world, according to Mr. Clayton, and can result in 25 billion forecasts every day.

Those end up, in one form or another, with consumers, whether delivered by a broadcaster as part of the local morning weather report; on an IBM website such as weather.com or wunderground.com or its familiar The Weather Channel app; or, for business customers, through a product tailored for specific markets and uses. But increasingly, in an approach Mr. Clayton calls cognitive computing, the company is using artificial intelligence and machine learning to combine meteorological observations with a variety of other types of data, and go beyond predicting the weather to enable specific responses.

This is often accomplished through IBMs Watsona suite of AI tools and apps named for the companys founder that famously beat two returning champions on Jeopardy! in 2011and in the IBM Cloud. The idea is to deliver specific insights to help people make better decisions, from whether to take along the umbrella for the day to whether to evacuate in advance of a storm.

For example, the company is working with Oncor, based in Texas and among the nations largest utilities, to help predict where vegetative growth is most likely to interfere with power lines which can cause blackouts and wildfiresallowing managers to better plan for preventative maintenance.

The Weather Company uses AI to analyze weather phenomena, location, season and time of day and other actors to provide 15-day local flu forecasts.

At Knight Transportation, a leading trucking company, a Watson-enabled service allows drivers to receive real-time audio alerts through their in-cab communication systems about how weather conditions will affect their planned routes. That means truckers, whose safety, delivery deadlines and cargo are regularly threatened by the effects of bad weather, can better avoid roads made dangerous by ice, fog or high winds, or seek shelter before hitting a storm.

The Weather Company is applying AI-powered predictive platforms to other industries as well, including agribusiness, insurance, aviation, retail and energy trading.

The Weather Company's flu tracker can help people take precautions during a local outbreak of illnesses.

On the consumer side, The Weather Company websites and apps can provide personalized information beyond, say, a prediction of a 75 percent chance of rain at 2 p.m. in a particular zip code. The Weather Channel app, for instance, is able to glean users interests from the alerts and services they opt into and determine what information they most want to see each day, like the best hours to go for a run or whether an allergy sufferer will have to contend with a high pollen count.

There is also a new tracker that analyses weather phenomena, location, season and time of day along with other factors to determine the likelihood that people are at risk for getting the flu. It then provides a 15-day flu forecast on the theory that people can take precautions like avoiding events or washing their hands more frequently during an outbreak.

It becomes a platform that you could use for pretty much any data type, any type of a health-type situationallergies, flu, arthritis flare-ups, said Chris Hill, chief information and technology officer for IBM Watson Media and Weather. Theres a lot of interesting dynamics of health and weather and weve built a generic engine that enables us to gain insights from different types of weather using data science.

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IBM GRAF Builds on The Weather Companys AI and Cloud Capabilities - Forbes