At HIMSS20 next month, two machine learning experts will show how machine learning algorithms are evolving to handle complex physiological data and drive more detailed clinical insights.

During surgery and other critical care procedures, continuous monitoring of blood pressure to detect and avoid the onset of arterial hypotension is crucial. New machine learning technology developed by Edwards Lifesciences has proven to be an effective means of doing this.

In the prodromal stage of hemodynamic instability, which is characterized by subtle, complex changes in different physiologic variables unique dynamic arterial waveform "signatures" are formed, which require machine learning and complex feature extraction techniques to be utilized.

Feras Hatib, director of research and development for algorithms and signal processing at Edwards Lifesciences, explained his team developed a technology that could predict, in real-time and continuously, upcoming hypertension in acute-care patients, using an arterial pressure waveforms.

We used an arterial pressure signal to create hemodynamic features from that waveform, and we try to assess the state of the patient by analyzing those signals, said Hatib, who is scheduled to speak about his work at HIMSS20.

His teams success offers real-world evidence as to how advanced analytics can be used to inform clinical practice by training and validating machine learning algorithms using complex physiological data.

Machine learning approaches were applied to arterial waveforms to develop an algorithm that observes subtle signs to predict hypotension episodes.

In addition, real-world evidence and advanced data analytics were leveraged to quantify the association between hypotension exposure duration for various thresholds and critically ill sepsis patient morbidity and mortality outcomes.

"This technology has been in Europe for at least three years, and it has been used on thousands of patients, and has been available in the US for about a year now," he noted.

Hatib noted similar machine learning models could provide physicians and specialists with information that will help prevent re-admissions or other treatment options, or help prevent things like delirium current areas of active development.

"In addition to blood pressure, machine learning could find a great use in the ICU, in predicting sepsis, which is critical for patient survival," he noted. "Being able to process that data in the ICU or in the emergency department, that would be a critical area to use these machine learning analytics models."

Hatib pointed out the way in which data is annotated in his case, defining what is hypertension and what is not is essential in building the machine learning model.

"The way you label the data, and what data you include in the training is critical," he said. "Even if you have thousands of patients and include the wrong data, that isnt going to help its a little bit of an art to finding the right data to put into the model."

On the clinical side, its important to tell the clinician what the issue is in this case what is causing hypertension.

"You need to provide to them the reasons that could be causing the hypertension this is why we complimented the technology with a secondary screen telling the clinician what is physiologically is causing hypertension," he explained. "Helping them decide what do to about it was a critical factor."

Hatib said in the future machine learning will be everywhere, because scientists and universities across the globe are hard at work developing machine learning models to predict clinical conditions.

"The next big step I see is going toward using this ML techniques where the machine takes care of the patient and the clinician is only an observer," he said.

Feras Hatib, along with Sibyl Munson of Boston Strategic Partners, will share some machine learning best practices during his HIMSS20 in a session, "Building a Machine Learning Model to Drive Clinical Insights." It's scheduled for Wednesday, March 11, from 8:30-9:30 a.m. in room W304A.

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Machine learning and clinical insights: building the best model - Healthcare IT News

It is much easier to apologize than it is to get permission.

jamesnoellert.com

The hacking culture has been the lifeblood of software engineering long before the move fast and break things mantra became ubiquitous of tech startups [1, 2]. Computer industry leaders from Chris Lattner [3] to Bill Gates recount breaking and reassembling radios and other gadgets in their youth, ultimately being drawn to computers for their hackability. Silicon Valley itself may have never become the worlds innovation hotbed if it were not for the hacker dojo started by Gordon French and Fred Moore, The Homebrew Club.

Computer programmers still strive to move fast and iterate things, developing and deploying reliable, robust software by following industry proven processes such as test-driven development and the Agile methodology. In a perfect world, programmers could follow these practices to the letter and ship pristine software. Yet time is money. Aggressive, business-driven deadlines pass before coders can properly finish developing software ahead of releases. Add to this the modern best practices of rapid-releases and hot-fixing (or updating features on the fly [4]), the bar for deployable software is even lower. A company like Apple even prides itself by releasing phone hardware with missing software features: the Deep Fusion image processing was part of an iOS update months after the newest iPhone was released [5].

Software delivery becoming faster is a sign of progress; software is still eating the world [6]. But its also subject to abuse: Rapid software processes are used to ship fixes and complete new features, but are also used to ship incomplete software that will be fixed later. Tesla has emerged as a poster child with over the air updates that can improve driving performance and battery capacity, or hinder them by mistake [7]. Naive consumers laud Tesla for the tech-savvy, software-first approach theyre bringing to the old-school automobile industry. Yet industry professionals criticize Tesla for their recklessness: A/B testing [8] an 1800kg vehicle on the road is slightly riskier than experimenting with a new feature on Facebook.

Add Tesla Autopilot and machine learning algorithms into the mix, and this becomes significantly more problematic. Machine learning systems are by definition probabilistic and stochastic predicting, reacting, and learning in a live environment not to mention riddled with corner cases to test and vulnerabilities to unforeseen scenarios.

Massive progress in software systems has enabled engineers to move fast and iterate, for better or for worse. Now with massive progress in machine learning systems (or Software 2.0 [9]), its seamless for engineers to build and deploy decision-making systems that involve humans, machines, and the environment.

A current danger is that the toolset of the engineer is being made widely available but the theoretical guarantees and the evolution of the right processes are not yet being deployed. So while deep learning has the appearance of an engineering profession it is missing some of the theoretical checks and practitioners run the risk of falling flat upon their faces.

In his recent book Reboot AI [10], Gary Marcus draws a thought provoking analogy between deep learning and pharmacology: Deep learning models are more like drugs than traditional software systems. Biological systems are so complex it is rare for the actions of medicine to be completely understood and predictable. Theories of how drugs work can be vague, and actionable results come from experimentation. While traditional software systems are deterministic and debuggable (and thus robust), drugs and deep learning models are developed via experimentation and deployed without fundamental understanding and guarantees. Too often the AI research process is first experiment, then justify results. It should be hypothesis-driven, with scientific rigor and thorough testing processes.

What were missing is an engineering discipline with principles of analysis and design.

Before there was civil engineering, there were buildings that fell to the ground in unforeseen ways. Without proven engineering practices for deep learning (and machine learning at large), we run the same risk.

Taking this to the extreme is not advised either. Consider the shift in spacecraft engineering the last decade: Operational efficiencies and the move fast culture has been essential to the success of SpaceX and other startups such as Astrobotic, Rocket Lab, Capella, and Planet.NASA cannot keep up with the pace of innovation rather, they collaborate with and support the space startup ecosystem. Nonetheless, machine learning engineers can learn a thing or two from an organization that has an incredible track record of deploying novel tech in massive coordination with human lives at stake.

Grace Hopper advocated for moving fast: That brings me to the most important piece of advice that I can give to all of you: if you've got a good idea, and it's a contribution, I want you to go ahead and DO IT. It is much easier to apologize than it is to get permission. Her motivations and intent hopefully have not been lost on engineers and scientists.

[1] Facebook Cofounder Mark Zuckerberg's "prime directive to his developers and team", from a 2009 interview with Business Insider, "Mark Zuckerberg On Innovation".

[2] xkcd

[3] Chris Lattner is the inventor of LLVM and Swift. Recently on the AI podcast, he and Lex Fridman had a phenomenal discussion:

[4] Hotfix: A software patch that is applied to a "hot" system; i.e., a fix to a deployed system already in use. These are typically issues that cannot wait for the next release cycle, so a hotfix is made quickly and outside normal development and testing processes.

[5]

[6]

[7]

[8] A/B testing is an experimental processes to compare two or more variants of a product, intervention, etc. This is very common in software products when considering e.g. colors of a button in an app.

[9] Software 2.0 was coined by renowned AI research engineer Andrej Karpathy, who is now the Director of AI at Tesla.

[10]

[11]

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Machine Learning Is No Place To Move Fast And Break Things - Forbes

The Internet of Things is gradually penetrating every aspect of our lives. With the growth in numbers of internet-connected sensors built into cars, planes, trains, and buildings, we can say it is everywhere. Be it smart thermostats or smart coffee makers, IoT devices are marching ahead into mainstream adoption.

But, these devices are far from perfect. Currently, there is a lot of manual input required to achieve optimal functionality there is not a lot of intelligence built-in. You must set your alarm, tell your coffee maker when to start brewing, and manually set schedules for your thermostat, all independently and precisely.

These machines rarely communicate with each other, and you are left playing the role of master orchestrator, a labor-intensive job.

Every time the IoT sensors gather data, there has to be someone at the backend to classify the data, process them and ensure information is sent out back to the device for decision making. If the data set is massive, how could an analyst handle the influx? Driverless cars, for instance, have to make rapid decisions when on autopilot and relying on humans is completely out of the picture. Here, Machine Learning comes to play.

Tapping into that data to extract useful information is a challenge thats starting to be met using the pattern-matching abilities of machine learning. Firms are increasingly feeding data collected by Internet of Things (IoT) sensors situated everywhere from farmers fields to train tracks into machine-learning models and using the resulting information to improve their business processes, products, and services.

In this regard, one of the most significant leaders is Siemens, whose Internet of Trains project has enabled it to move from simply selling trains and infrastructure to offering a guarantee its trains will arrive on time.

Through this project, the company has embedded sensors in trains and tracks in selected locations in Spain, Russia, and Thailand, and then used the data to train machine-learning models to spot tell-tale signs that tracks or trains may be failing. Having granular insights into which parts of the rail network are most likely to fail, and when, has allowed repairs to be targeted where they are most needed a process called predictive maintenance. That, in turn, has allowed Siemens to start selling what it calls outcome as a service a guarantee that trains will arrive on-time close to 100 percent of the time.

Besides, Thyssenkrupp is one of the earliest firms to pair IoT sensor data with machine learning models, which runs 1.1 million elevators worldwide and has been feeding data collected by internet-connected sensors throughout its elevators into trained machine-learning models for several years. Such models provide real-time updates on the status of elevators and predict which are likely to fail and when, allowing the company to target maintenance where its needed, reducing elevator outages and saving money on unnecessary servicing. Similarly, Rolls-Royce collects more than 70 trillion data points from its engines, feeding that data into machine-learning systems that predict when maintenance is required.

In a recent report, IDC analysts Andrea Minonne, Marta Muoz, Andrea Siviero say that applying artificial intelligence the wider field of study that encompasses machine learning to IoT data is already delivering proven benefits for firms.

Given the huge amount of data IoT connected devices collect and analyze, AI finds fertile ground across IoT deployments and use cases, taking analytics level to uncovered insights to help lower operational costs, provide better customer service and support, and create product and service innovation, they say.

According to IDC, the most common use cases for machine learning and IoT data will be predictive maintenance, followed by analyzing CCTV surveillance, smart home applications, in-store contextualized marketing and intelligent transportation systems.

That said, companies using AI and IoT today are outliers, with many firms neither collecting large amounts of data nor using it to train machine-learning models to extract useful information.

Were definitely still in the very early stages, says Mark Hung, research VP at analyst Gartner.

Historically, in a lot of these use cases in the industrial space, smart cities, in agriculture people have either not been gathering data or gathered a large trove of data and not really acted on it, Hung says. Its only fairly recently that people understand the value of that data and are finding out whats the best way to extract that value.

The IDC analysts agree that most firms are yet to exploit IoT data using machine learning, pointing out that a large portion of IoT users are struggling to go beyond a mere data collection due to a lack of analytics skills, security concerns, or simply because they dont have a forward-looking strategic vision.

The reason machine learning is currently so prominent is because of advances over the past decade in the field of deep learning a subset of ML. These breakthroughs were applied to areas from computer vision to speech and language recognition, allowing computers to see the world around them and understand human speech at a level of accuracy not previously possible.

Machine learning uses different approaches for harnessing trainable mathematical models to analyze data, and for all the headlines ML receives, its also only one of many different methods available for interrogating data and not necessarily the best option.

Dan Bieler, the principal analyst at Forrester, says: We need to recognize that AI is currently being hyped quite a bit. You need to look very carefully whether itd generate the benefits youre looking for whether itd create the value that justifies the investment in machine learning.

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Deploying Machine Learning to Handle Influx of IoT Data - Analytics Insight

Advances to ReversingLabs Titanium Platform Deliver Transparent and Trusted Malware Insights that Address Security Skills Gap

CAMBRIDGE, Mass., Feb. 18, 2020 (GLOBE NEWSWIRE) -- ReversingLabs, a leading provider of explainable threat intelligence solutions today announced new and enhanced capabilities for its Titanium Platform, including new machine learning algorithm models, explainable classification and out-of-the-box security information and event management (SIEM) plug-ins, security, orchestration, automation and response (SOAR) playbooks, and MITRE ATT&CK Framework support. Introducing a new level of threat intelligence, the Titanium Platform now delivers explainable insights and verification that better support humans in the incident response decision making process. ReversingLabs has been named as a ML-Based Machine Learning Binary Analysis Sample Provider within Gartners 2019 Emerging Technologies and Trends Impact Radar: Security1.. ReversingLabs will showcase its new Titanium Platform at RSA 2020, February 24-28 in San Francisco, Moscone Center, Booth #3311 in the South Expo.

As digital initiatives continue to gain momentum, companies are exposed to an increasing number of threat vectors fueled by a staggering volume of data that contains countless malware infected files and objects, demanding new requirements from the IT teams that support them, said Mario Vuksan, CEO and Co-founder, ReversingLabs. Its no wonder security operations teams struggle to manage incident response. Combine the complexity of threats with blind black box detection engine verdicts, and a lack of analyst experience, skill and time, and teams are crippled by their inability to effectively understand and take action against these increased risks. The current and future threat landscape requires a different approach to threat intelligence and detection that automates time-intensive threat research efforts with the level of detail analysts need to better understand events, improve productivity and refine their skills.

According to Gartners Emerging Technologies and Trends Impact Radar: Security, Gartner estimates that ML-based file analysis has grown at 35 percent over the past year in security technology products with endpoint products being first movers to adopt this new technology.2

Black Box to Glass Box VerdictsBecause signature, AI and machine learning-based threat classifications from black box detection engines come with little to no context, security analysts are left in the dark as to why a verdict was determined, negatively impacting their ability to verify threats, take informed action and extend critical job skills. That lack of context and transparency propelled ReversingLabs to develop a new glass box approach to threat intelligence and detection designed to better inform human understanding first. Security operations teams using ReversingLabs Titanium Platform with patent-pending Explainable Machine Learning can automatically inspect, unpack, and classify threats as before, but with the added capability of verifying these threats in context with transparent, easy to understand results. By applying new machine learning algorithms to identify threat indicators, ReversingLabs enables security teams to more quickly and accurately identify and classify unknown threats.

Key FeaturesAvailable now with Explainable Machine Learning, ReversingLabs platform inspires confidence in threat detection verdicts amongst security operations teams through a transparent and context-aware diagnosis, automating manual threat research with results humans can interpret to take informed action on zero day threats, while simultaneously fueling continuous education and the upskilling of analysts. ReversingLabs Explainable Machine Learning is based on machine learning-based binary file analysis, providing high-speed analysis, feature extraction and classification that can be used to enhance telemetry provided to incident response analysts. Key features of ReversingLabs updated platform include:

Effective machine learning results depend on having the right volume, structure, and quality of data to convert information into a relevant finding, said Vijay Doradla, Chief Business Officer at SparkCognition. With access to ReversingLabs cloud extensive repository, we have the breadth, depth, and scale of data necessary to train our machine learning models. Accurate classification and detection of threats fuels the machine learning-driven predictive security model leveraged in our DeepArmor next-generation endpoint protection platform.

1, 2 Gartner, Emerging Technologies and Trends Impact Radar: Security, Lawrence Pingree, et al, 13 November 2019

About ReversingLabsReversingLabs helps Security Operations Center (SOC) teams identify, detect and respond to the latest attacks, advanced persistent threats and polymorphic malware by providing explainable threat intelligence into destructive files and objects.ReversingLabs technology is used by the worlds most advanced security vendors and deployed across all industries searching for a better way to get at the root of the web, mobile, email, cloud, app development and supply chain threat problem, of which files and objects have become major risk contributors.

ReversingLabs Titanium Platform provides broad integration support with more than 4,000 unique file and object formats, speeds detection of malicious objects through automated static analysis, prioritizing the highest risks with actionable detail in only .005 seconds. With unmatched breadth and privacy, the platform accurately detects threats through explainable machine learning models, leveraging the largest repository of malware in the industry, containing more than 10 billion files and objects. Delivering transparency and trust, thousands of human readable indicators explain why a classification and threat verdict was determined, while integrating at scale across the enterprise with connectors that support existing SIEM, SOAR, threat intelligence platform and sandbox investments, reducing incident response time for SOC analysts, while providing high priority and detailed threat information for hunters to take quick action. Learn more at https://www.reversinglabs.com, or connect on LinkedIn or Twitter.

Media Contact: Jennifer Balinski, Guyer Groupjennifer.balinski@guyergroup.com

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ReversingLabs Releases First Threat Intelligence Platform with Explainable Machine Learning to Automate Incident Response Processes with Verified...

GREENWOOD VILLAGE, Colo., Feb. 17, 2020 /PRNewswire-PRWeb/ --Driven by a passion for learning and all things data science, Brian Burch has cultivated an exemplary career in building solutions which solve business problems across multiple industries including cybersecurity, financial services, retail, telecommunications, and aerospace. In addition to having a strong technical background across a broad range of vertical markets, Brian brings deep expertise in the areas of Artificial Intelligence and Machine Learning (AI/ML), Software Engineering, and Product Management.

"We are excited about Brian Burch joining the zvelo leadership team," explains zvelo CEO, Jeff Finn. "zvelo is quickly gaining momentum with tremendous growth opportunities built upon the zveloAI platform. Brian brings an impressive background in AI/ML and data science to further zvelo's leadership for URL classification, objectionable and malicious detection and his passion aligns perfectly with zvelo's mission to improve internet safety and security."

From large organizations like CenturyLink and Regions Bank to successful startups like StorePerform Technologies and Cognilytics, Brian has a proven history of leveraging his vast experience in key leadership roles to advance business goals through a fully-immersed, hands-on approach.

"I'm especially excited about combining zvelo's strong web categorization technologies with the latest advances in AI/ML to identify malicious websites, phishing URLs, and malware distribution infrastructure, and play a key role in supporting the mission to make the internet safer for everyone," stated Burch.

About zvelo, Inc. zvelo is a leading provider of web content classification and detection of objectionable, malicious and threat detection services with a mission of making the Internet safer and more secure. zvelo combines advanced artificial intelligence-based contextual categorization with sophisticated malicious and phishing detection capabilities that customers integrate into network and endpoint security, URL and DNS filtering, brand safety, contextual targeting, and other applications where data quality, accuracy, and detection rates are critical.

Learn more at: https://www.zvelo.com

Corporate Information: zvelo, Inc. 8350 East Crescent Parkway, Suite 450 Greenwood Village, CO 80111 Phone: (720) 897-8113 zvelo.com or pr@zvelo.com

SOURCE zvelo

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Brian Burch Joins zvelo as Head of Artificial Intelligence and Machine Learning to Drive New Growth Initiatives - Benzinga

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Global Machine Learning in Automobile Market Insight Growth Analysis on Volume, Revenue and Forecast to 2019-2025 - News Parents

Humans are error-prone and biased, but that doesnt mean that algorithms are necessarily better. Still, the tech is already making important decisions about your life and potentially ruling over which political advertisements you see, how your application to your dream job is screened, how police officers are deployed in your neighborhood, and even predicting your homes risk of fire.

But these systems can be biased based on who builds them, how theyre developed, and how theyre ultimately used. This is commonly known as algorithmic bias. Its tough to figure out exactly how systems might be susceptible to algorithmic bias, especially since this technology often operates in a corporate black box. We frequently dont know how a particular artificial intelligence or algorithm was designed, what data helped build it, or how it works.

Typically, you only know the end result: how it has affected you, if youre even aware that AI or an algorithm was used in the first place. Did you get the job? Did you see that Donald Trump ad on your Facebook timeline? Did a facial recognition system identify you? That makes addressing the biases of artificial intelligence tricky, but even more important to understand.

When thinking about machine learning tools (machine learning is a type of artificial intelligence), its better to think about the idea of training. This involves exposing a computer to a bunch of data any kind of data and then that computer learns to make judgments, or predictions, about the information it processes based on the patterns it notices.

For instance, in a very simplified example, lets say you wanted to train your computer system to recognize whether an object is a book, based on a few factors, like its texture, weight, and dimensions. A human might be able to do this, but a computer could do it more quickly.

To train the system, you show the computer metrics attributed to a lot of different objects. You give the computer system the metrics for every object, and tell the computer when the objects are books and when theyre not. After continuously testing and refining, the system is supposed to learn what indicates a book and, hopefully, be able to predict in the future whether an object is a book, depending on those metrics, without human assistance.

That sounds relatively straightforward. And it might be, if your first batch of data was classified correctly and included a good range of metrics featuring lots of different types of books. However, these systems are often applied to situations that have much more serious consequences than this task, and in scenarios where there isnt necessarily an objective answer. Often, the data on which many of these decision-making systems are trained or checked are often not complete, balanced, or selected appropriately, and that can be a major source of although certainly not the only source of algorithmic bias.

Nicol Turner-Lee, a Center for Technology Innovation fellow at the Brookings Institution think tank, explains that we can think about algorithmic bias in two primary ways: accuracy and impact. An AI can have different accuracy rates for different demographic groups. Similarly, an algorithm can make vastly different decisions when applied to different populations.

Importantly, when you think of data, you might think of formal studies in which demographics and representation are carefully considered, limitations are weighed, and then the results are peer-reviewed. Thats not necessarily the case with the AI-based systems that might be used to make a decision about you. Lets take one source of data everyone has access to: the internet. One study found that, by teaching an artificial intelligence to crawl through the internet and just reading what humans have already written the system would produce prejudices against black people and women.

Another example of how training data can produce sexism in an algorithm occurred a few years ago, when Amazon tried to use AI to build a rsum-screening tool. According to Reuters, the companys hope was that technology could make the process of sorting through job applications more efficient. It built a screening algorithm using rsums the company had collected for a decade, but those rsums tended to come from men. That meant the system, in the end, learned to discriminate against women. It also ended up factoring in proxies for gender, like whether an applicant went to a womens college. (Amazon says the tool was never used and that it was nonfunctional for several reasons.)

Amid discussions of algorithmic biases, companies using AI might say theyre taking precautions, taking steps to use more representative training data and regularly auditing their systems for unintended bias and disparate impact against certain groups. But Lily Hu, a doctoral candidate at Harvard in applied mathematics and philosophy who studies AI fairness, says those arent assurances that your system will perform fairly in the future.

You dont have any guarantees because your algorithm performs fairly on your old dataset, Hu told Recode. Thats just a fundamental problem of machine learning. Machine learning works on old data [and] on training data. And it doesnt work on new data, because we havent collected that data yet.

Still, shouldnt we just make more representative datasets? That might be part of the solution, though its worth noting that not all efforts aimed at building better data sets are ethical. And its not just about the data. As Karen Hao of the MIT Tech Review explains, AI could also be designed to frame a problem in a fundamentally problematic way. For instance, an algorithm designed to determine creditworthiness thats programmed to maximize profit could ultimately decide to give out predatory, subprime loans.

Heres another thing to keep in mind: Just because a tool is tested for bias which assumes that engineers who are checking for bias actually understand how bias manifests and operates against one group doesnt mean it is tested for bias against another type of group. This is also true when an algorithm is considering several types of identity factors at the same time: A tool may deemed fairly accurate on white women, for instance, but that doesnt necessarily mean it works with black women.

In some cases, it might be impossible to find training data free of bias. Take historical data produced by the United States criminal justice system. Its hard to imagine that data produced by an institution rife with systemic racism could be used to build out an effective and fair tool. As researchers at New York University and the AI Now Institute outline, predictive policing tools can be fed dirty data, including policing patterns that reflect police departments conscious and implicit biases, as well as police corruption.

So you might have the data to build an algorithm. But who designs it, and who decides how its deployed? Who gets to decide what level of accuracy and inaccuracy for different groups is acceptable? Who gets to decide which applications of AI are ethical and which arent?

While there isnt a wide range of studies on the demographics of the artificial intelligence field, we do know that AI tends to be dominated by men. And the high tech sector, more broadly, tends to overrepresent white people and underrepresent black and Latinx people, according to the Equal Employment Opportunity Commission.

Turner-Lee emphasizes that we need to think about who gets a seat at the table when these systems are proposed, since those people ultimately shape the discussion about ethical deployments of their technology.

But theres also a broader question of what questions artificial intelligence can help us answer. Hu, the Harvard researcher, argues that for many systems, the question of building a fair system is essentially nonsensical, because those systems try to answer social questions that dont necessarily have an objective answer. For instance, Hu says algorithms that claim to predict a persons recidivism dont ultimately address the ethical question of whether someone deserves parole.

Theres not an objective way to answer that question, Hu says. When you then insert an AI system, an algorithmic system, [or] a computer, that doesnt change the fundamental context of the problem, which is that the problem has no objective answer. Its fundamentally a question of what our values are, and what the purpose of the criminal justice system is.

That in mind, some algorithms probably shouldnt exist, or at least they shouldnt come with such a high risk of abuse. Just because a technology is accurate doesnt make it fair or ethical. For instance, the Chinese government has used artificial intelligence to track and racially profile its largely Muslim Uighur minority, about 1 million of whom are believed to be living in internment camps.

One of the reasons algorithmic bias can seem so opaque is because, on our own, we usually cant tell when its happening (or if an algorithm is even in the mix). That was one of the reasons why the controversy over a husband and wife who both applied for an Apple Card and got widely different credit limits attracted so much attention, Turner-Lee says. It was a rare instance in which two people, who at least appeared to be exposed to the same algorithm and could easily compare notes. The details of this case still arent clear, though the companys credit card is now being investigated by regulators.

But consumers being able to make apples-to-apples comparisons of algorithmic results are rare, and thats part of why advocates are demanding more transparency about how systems work and their accuracy. Ultimately, its probably not a problem we can solve on the individual level. Even if we do understand that algorithms can be biased, that doesnt mean companies will be forthright in allowing outsiders to study their artificial intelligence. Thats created a challenge for those pushing for more equitable, technological systems. How can you critique an algorithm a sort of black box if you dont have true access to its inner workings or the capacity to test a good number of its decisions?

Companies will claim to be accurate, overall, but wont always reveal their training data (remember, thats the data that the artificial intelligence trains on before evaluating new data, like, say, your job application). Many dont appear to be subjecting themselves to audit by a third-party evaluator or publicly sharing how their systems fare when applied to different demographic groups. Some researchers, such as Joy Buolamwini and Timnit Gebru, say that sharing this demographic information about both the data used to train and the data used to check artificial intelligence should be a baseline definition of transparency.

We will likely need new laws to regulate artificial intelligence, and some lawmakers are catching up on the issue. Theres a bill that would force companies to check their AI systems for bias through the Federal Trade Commission (FTC). And legislation has also been proposed to regulate facial recognition, and even to ban the technology from federally assisted public housing.

But Turner-Lee emphasizes that new legislation doesnt mean existing laws or agencies dont have the power to look over these tools, even if theres some uncertainty. For instance, the FTC oversees deceptive acts and practices, which could give the agency authority over some AI-based tools.

The Equal Employment Opportunity Commission, which investigates employment discrimination, is reportedly looking into at least two cases involving algorithmic discrimination. At the same time, the White House is encouraging federal agencies that are figuring out how to regulate artificial intelligence to keep technological innovation in mind. That raises the challenge of whether the government is prepared to study and govern this technology, and figure out how existing laws apply.

You have a group of people that really understand it very well, and that would be technologists, Turner-Lee cautions, and a group of people who dont really understand it at all, or have minimal understanding, and that would be policymakers.

Thats not to say there arent technical efforts to de-bias flawed artificial intelligence, but its important to keep in mind that the technology wont be a solution to fundamental challenges of fairness and discrimination. And, as the examples weve gone through indicate, theres no guarantee companies building or using this tech will make sure its not discriminatory, especially without a legal mandate to do so. It would seem its up to us, collectively, to push the government to rein in the tech and to make sure it helps us more than it might already be harming us.

Open Sourced is made possible by Omidyar Network. All Open Sourced content is editorially independent and produced by our journalists.

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Algorithms and bias, explained - Vox.com

About the project

The vision of Norwegian Public Roads Administration (NPRA, Norw.: Statens vegvesen) is to contribute to national goals for the transportation system. These goals are safety, promoting added value in society, and promoting change towards lower global emissions. The road system in Norway is large and complex, and the geography of Norway raises a range of challenges in this area with respect to maintenance which will be given priority compared to new large road investments in the coming years.

The Norwegian National Transport Plan is aimed towards promoting mobility, traffic safety, climatic and environmental conditions. To ensure a high-quality road infrastructure it is important to choose effective maintenance actions within the areas of operations, maintenance and rehabilitation. In particular, the development of new technology and new digital concepts is essential to enable more efficient monitoring and analysis of road traffic and road network conditions.

There is a technological shift taking place towards a more digitalized society. This technological shift has the potential to contribute to the overall goals of safety, low emissions and increased resource efficiency. The vision of NTNU is Knowledge for a Better World and is actively pursuing this goal across education, research and innovation. In the area of transportation, NTNU conducts extensive activity in several relevant engineering fields connected to infrastructure, maintenance and digitalization.

NPRA established a research and development project with the title "Smarter maintenance". This project on road maintenance and infrastructure will involve close cooperation between the areas of research expertise in civil, transport and structural engineering, technology, digitalization and maintenance, and economics. This cooperation is organized within three thematic areas: (1) Condition registration, data analysis and modelling; (2) Big data, artificial intelligence, strategic analysis and planning; and (3) Maintenance, social economics and innovation. There is both a substantial need and many opportunities for innovation in this research program which will bring together 7 PhD candidates across several engineering and cognate fields. Together, they will seek to solve specific challenges connected to the maintenance of transportation infrastructure.

These positions will be grouped into research clusters that will ensure close cooperation between PhD-candidates, supervisors, NPRA-experts and master/bachelor students.

We are seeking motivated candidates to work in a multidisciplinary and innovative setting of national and international importance.

About the position

We have a vacancy for a PhD position at the Department of Computer Science. The work will be carried out in close collaboration with domain experts from the Norwegian Public Roads Administration (NPRA) and the position will be affiliated with the Norwegian Open AI Lab (NAIL).

The candidate will perform research on next-generation AI and computer vision methods related to maintenance of road infrastructure. Key research topics that will be investigated in this PhD project are:

The position reports to Professor Frank Lindseth.

Main duties and responsibilities

Qualification requirements

Essential requirements:

The PhD-position's main objective is to qualify for work in research positions. The qualification requirement is completion of a masters degree or second degree (equivalent to 120 credits) with a strong academic background inComputer Scienceor equivalent education with a grade of B or better in terms of NTNUs grading scale. Applicants with no letter grades from previous studies must have an equally good academic foundation. Applicants who are unable to meet these criteria may be considered only if they can document that they are particularly suitable candidates for education leading to a PhD degree. Key qualifications are:

Candidates completing their MSc-degree in the Spring 2020 are encouraged to apply. The position is also open for integrated PhD for NTNU students starting their final year of their Masters Degree in Autumn 2020.

Desirable qualifications:

The appointment is to be made in accordance with the regulations in force concerning State Employees and Civil Servants and national guidelines for appointment as PhD, postdoctor and research assistant

NTNU is committed to following evaluation criteria for research quality according to The San Francisco Declaration on Research Assessment - DORA.

Personal characteristics

In the evaluation of which candidate is best qualified, emphasis will be placed on education, experience and personal suitability, as well as motivation, in terms of the qualification requirements specified in the advertisement

We offer

Salary and conditions

PhD candidates are remunerated in code 1017, and are normally remunerated at gross from NOK 479 600 before tax peryear. From the salary, 2 % is deducted as a contribution to the Norwegian Public Service Pension Fund.

The period of employment is3 years without required duties. Appointment to a PhD position requires admission to the PhD programme in Computer Sience.

As a PhD candidate, you undertake to participate in an organized PhD programme during the employment period. A condition of appointment is that you are in fact qualified for admission to the PhD programme within three months.

The engagement is to be made in accordance with the regulations in force concerning State Employees and Civil Servants, and the acts relating to Control of the Export of Strategic Goods, Services and Technology. Candidates who by assessment of the application and attachment are seen to conflict with the criterias in the latter law will be prohibited from recruitment to NTNU. After the appointment you must assume that there may be changes in the area of work.

General information

A good work environment is characterized by diversity. We encourage qualified candidates to apply, regardless of their gender, functional capacity or cultural background. Under the Freedom of Information Act (offentleglova), information about the applicant may be made public even if the applicant has requested not to have their name entered on the list of applicants.

The national labour force must reflect the composition of the population to the greatest possible extent, NTNU wants to increase the proportion of women in its scientific posts. Women are international schools) and possibilities to enjoy nature, culture and family life encouraged to apply. Furthermore, Trondheim offers great opportunities for education (including (http://trondheim.com/). Having a population of 200000, Trondheim is a small city by international standards with low crime rates and little pollution. It also has easy access to a beautiful countryside with mountains and a dramatic coastline.

Questions about the position can be directed to Professor Frank Lindseth, phone number+47 928 09 372, e-mail frankl@ntnu.no

The application must contain:

Publications and other academic works that the applicant would like to be considered in the evaluation must accompany the application. Joint works will be considered. If it is difficult to identify the individual applicant's contribution to joint works, the applicant must include a brief

Please submit your application electronically via jobbnorge.no with your CV, diplomas and certificates. Applications submitted elsewhere will not be considered. Diploma Supplement is required to attach for European Master Diplomas outside Norway. Chinese applicants are required to provide confirmation of Master Diploma from China Credentials Verification (CHSI): http://www.chsi.com.cn/en/).

Applicants invited for interview must include certified copies of transcripts and reference letters.

Please refer to the application number 2020/5928 when applying.

Application deadline:07.03.2020

NTNU - knowledge for a better world

The Norwegian University of Science and Technology (NTNU) creates knowledge for a better world and solutions that can change everyday life.

Faculty of Information Technology and Electrical Engineering

The Faculty of Information Technology and Electrical Engineering is Norways largest university environment in ICT, electrical engineering and mathematical sciences. Our aim is to contribute to a smart, secure and sustainable future. We emphasize high international quality in research, education,innovation, dissemination and outreach. The Faculty consists of seven departments and the Faculty Administration.

Deadline 7th March 2020EmployerNTNU - Norwegian University of Science and TechnologyMunicipality TrondheimScope FulltimeDuration TemporaryPlace of service Trondheim

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PhD in Machine Learning and Computer Vision for Smart Maintenance of Road Infrastructure job with NORWEGIAN UNIVERSITY OF SCIENCE & TECHNOLOGY...

Lecturer/Senior Lecturer in Artificial Intelligence and / or Machine learning

Job number ACAD104467Division/School School of Computer Science, Electrical and Electronic Engineering and Engineering MathsContract type Open EndedWorking pattern Full timeSalary 38,017 - 59,135 per annumClosing date for applications 11-Mar-2020

The Department of Computer Science, University of Bristol, is seeking to appoint a number of Lecturers (analogous to Assistant Professor) or Senior Lecturers in Artificial Intelligence and / or Machine learning, the level of appointment depending on the experience of the successful candidate.

You will be expected to both deliver outstanding teaching and undertake internationally leading research as well as carrying out appropriate administrative tasks. There are opportunities to play a significant role in shaping and leading Bristols activities in AI and Data Science, including the new UKRI Centre for Doctoral Training in Interactive AI. Teaching responsibility will cover areas including: data-driven computer science, machine learning and artificial intelligence, for advanced undergraduates as well as postgraduates.

The Department of Computer Science is an international centre of excellence in the foundations and applications of computing, ranked 4th in the UK for research intensity by the 2014 REF. The Department is already home to significant activity in Artificial Intelligence, Machine Learning and Data Science both within the Intelligent System Laboratory research group, as well as closely associated neighbouring research groups in Computer Vision and Robotics. The University of Bristol is a leading institution among the UKs Russell Group Universities and is regularly placed among the top-ranking institutions in global league tables.

We are located in the centre of Bristol, consistently recognised as one of the UK most-liveable cities.

Informal enquires are welcome and can be directed to: Prof. Seth Bullock, Head of the Computer Science department (seth.bullock@bristol.ac.uk), and Prof. Peter Flach, Professor of Artificial Intelligence (peter.flach@bristol.ac.uk).

The posts are being offered on a full-time, open-ended contract. Recruitment supplement scheme available up to 5K.

The closing date for applications is 23:59 on Wednesday 11th March and interviews are expected to take place in week commencing 6th April.

We welcome applications from all members of our community and are particularly encouraging those from diverse groups, such as members of the LGBT+ and BAME communities, to join us.

Read more here:
Lecturer/Senior Lecturer in Artificial Intelligence and / or Machine learning job with UNIVERSITY OF BRISTOL | 196709 - Times Higher Education (THE)

LOS GATOS, Calif., Feb. 18, 2020 (GLOBE NEWSWIRE) -- via NetworkWire --iMerit, a leading data annotation and enrichment company, is headed into2020 with expansion plans, new innovation and new funding for itshuman-in-the-loop AItechnology platform. The company has attracted $20 millionin Series B funding led by CDC Group, the UKsleading publicly-owned impact investor.This investment, which also includes participation fromexistinginvestors, will be used to continue innovation for the companys proprietary AIplatform that delivers 100% quality control and over 98% accuracy.

The funding will also be used to expand its advanced workforcefrom 3,000 employees across the US,India and Bhutan to 10,000 global employees by 2023. It is the latest signthat iMeritshigh-quality datasets for artificial intelligence (AI) and machine learningare leading the industry and achieving the highest security certification. Thecompanys dataannotation and enrichmentspecialists work across nine securecenters globally. They provide solutionsacross multiple markets including automotive, healthcare, e-commerce, finance,media and entertainment, and government. iMerit has been growing at over 100%for 3years, has been cash positive for the last 2 years, and is continuing todifferentiate from the rest of the market.

This investmentvalidates our belief that the growth in artificial intelligence and machinelearning is best serviced by a full-time, specialist workforce thatcontinuously learns and grows with the technology, saysiMerit CEO and founderRadha R. Basu, and CDC Group shares this belief. This new funding will enableiMerit to continue to provide enterprise-scale and quality to a large clientbase in a fast-growing andevolving market.Our investment in iMerit underlinesour commitment to back companies that are creating skilled jobs, particularlyfor women, in countries where they are most needed, says Nick ODonohoe, CEO, CDC Group.Advancesin AI technology are normally seen as a threat to jobs. iMerit has demonstratedthat the opposite is true. The technology sector has an incredibly importantrole to play in supporting the UNsSustainable Development Goals and in that regard iMerit is a true pioneer.

iMerits contributions toglobal AI initiatives in 2020 will include:

CDCsmission and iMeritsjourney align very well, says DD Ganguly, President of iMerit USA. Workingwith an organization, like CDC, that prioritizes an advanced, inclusive andgender-balanced workforce isperfect for iMerit. The collaboration will enableiMerit to continue to build a specialized, profitable, high growth business,with a customizable and agile technology platform, that will foster strongcustomer loyaltyin a cutting-edge sector.About iMeritiMerit's Artificial Intelligence and Machine Learning platformpowers advanced algorithms in Machine Learning, Computer Vision, NaturalLanguage Understanding, e-Commerce,Augmented Reality and Data Analytics. Itworks on data for transformative technologies such as advancing cancer cellresearch, optimizing crop yields and training driverless cars tounderstandtheir environment. The company drives social and economic change by tappinginto an under-resourced talent pool and creating digital inclusion. The teamconsists of3000 full-time staff, with more than 50% being women. The companys initial investorsareOmidyar Network, Michael and Susan Dell Foundation, and Khosla Impact. Formore information, visit:www.imerit.net.

About CDC GroupCDC Group is the worldsfirst impact investor with over 70 years of experience of successfullysupporting the sustainable, long-term growth of businesses in Africa and SouthAsia. CDC is a key advocate for the adoption of renewable energy inAfrica and South Africa in the fight against climate change and a UK championof the UNsSustainable Development Goals the global blueprint toachieve a better andmore sustainable future for us all.The company has investments in over 1,200 businesses in emergingeconomies and a total portfolio value of 5.8bn. This year CDC will invest over$1.5bn in companies in Africaand Asia with a focus on fighting climate change,empowering women and creating new jobs and opportunities for millions ofpeople.CDC is funded by the UK government and all proceeds from itsinvestments are reinvested to improve the lives of millions of people in Africaand South Asia.CDCsexpertise makes it the perfect partner for private investors looking to devotecapital to making a measurable environmental and social impact in countriesmost in need of investment.CDC provides flexible capital in all its forms, includingequity, debt, mezzanine and guarantees, to meet businessesneeds.It can invest across all sectors, butprioritizes those that help further development,such as infrastructure,financial institutions, manufacturing, and constructions. Find out more atwww.cdcgroup.com.

Media ContactAndreaHeuer at Consort PartnersSan Franciscoandreah@consortpartners.com

For further information please contactAndrew Murray-Watson123 Victoria Street, London, SW1E 6DEM. +44 (0) 7515 695232amurray-watson@cdcgroup.com

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iMerit Leads off 2020 With New AI Innovation Initiatives and Funding - GlobeNewswire

Apple thinks it can improve location accuracy by applying machine learning to Kalman estimation filters, a just-published patent application reveals. Kalman filters are popularly used in GPS and robotic motion tracking applications. And, now Apple wants to use machine learning along with Kalman filters to bring the accuracy of positioning down to centimetre-level.

According to the patent application, Apple proposes:

The device, say an iPhone would generate a machine learning model, for example, by comparing GNSS position estimates (or estimated measurement errors) with corresponding reference position estimates (where the reference positions correspond to ground truth data).

In one or more implementations, the ground truth data may be better (e.g., significantly better) than what a mobile device alone can perform in most non-aided mode(s) of operation. For example, a mobile phone in a car may be significantly better aided than a pedestrian device, because the motion model for a vehicle is more constrained, and has aiding data in the form of maps and sensor inputs.

Tall buildings and tree cover can confuse the positioning systems to accurately locate the user. So, Apple wants to generate machine learning models on the device that would predict the users location based on its training as well as a reference position.

Today even Apples rivals praise Apple for it has done to the electronics industry. In an in-depth CNBC interview with Huaweis founder and CEO Ren Zhengfei earlier this year he spoke about how Apple has revolutionised the era of the Internet. In his ascent, however, Apple has put many traditional companies to dust.

According to a 2018 CNBCs report, there has been a dramatic decline in worldwide shipments of cameras. The chart above from Statista illustrates this fall, which also coincides with the peaking of Apple iPhone and its ever-improving camera.

So, the companies those who outsource their GPS improving services will be watching the new ML-based GPS patent closely or even might be rushing to build something of their own. However, this might not be the case in this modern era of mega collaborations.

Last month we saw one of the biggest corporate crossovers of the 21st century, when the tech giants, Amazon, Apple and Google, along with others announced their plans to develop compatible smart home products together.

Gone are the days where companies build something up from scratch (with the exception of Tesla). If your rival company is good at something you are not, then you either buy a startup that works solely on that technology or join hands with the rival. So, Apples patent to improve GPS in the upcoming 5G era might receive a warm welcome.

Of course, there always will be a debate about whether one should patent widely used technology, which can hand over infinite leverage to a single entity.

That said, the last two years has since increased attention in seeking patents over ML-based techniques. Last year, it was Google, which has been in the news for patenting machine learning techniques such as batch normalisation. Companies like Google and Apple have been leading the AI race for quite some time. It can also be possible if it is a routine to apply for a patent for their innovations and this new-found obsession over ML patent news is due to the rising popularity of AI globally.

At the end of the day, it comes down to whether you should risk years worth of intellectual property to a potential patent troll or safeguard it through patenting and then democratise the technology to the masses. It has been the latter, for many years and we have to wait and watch if machine learning-based patents find an exception as we go forward.

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Apple Patents ML Based Navigation System For Its Maps, But Why? - Analytics India Magazine