MENLO PARK, Calif., Aug. 6, 2020 /PRNewswire/ -- Ensighten, the leader in client-side website security and privacy compliance enforcement, today announced increased investment into threat intelligence powered by machine learning. The new threat intelligence will focus specifically on client-side website threats with a mandate of discovering new methods as well as actively monitoring ongoing attacks against organizations.

Client-side attacks such as web skimming are now one of the leading threat vectors for data breaches and with a rapid acceleration of the digital transformation, businesses are facing a substantially increased risk. With privacy regulations, including the CCPA and GDPR, penalizing organizations for compromised customer data, online businesses of all sizes are facing significant security challenges due to the number of organized criminal groups using sophisticated malware.

"We have seen online attacks grow in both intensity and complexity over the past couple of years, with major businesses having their customers' data stolen," said Marty Greenlow, CEO of Ensighten. "One of the biggest challenges facing digital security is that these attacks happen at the client side in the customers' browser, making them very difficult to detect and often run for significant periods of time. By leveraging threat intelligence and machine learning, our customers will benefit from technology which dynamically adapts to the growing threat." Ensighten already provides the leading client-side website security solution to prevent accidental and malicious data leakage, and by expanding its threat intelligence, not only will it benefit its own technology, but also the security community in general. "We are a pioneer in website security, and we need to continue to lead the way," said Greenlow.

Ensighten's security technology is used by the digital marketing and digital security teams of some of the world's largest brands to protect their website and applications against malicious threats. This new threat intelligence initiative will enable further intelligence-driven capabilities and machine learning will drive automated rules, advanced data analytics, and more accurate identification. "Threat intelligence has always been part of our platform," said Jason Patel, Ensighten CTO, "but this investment will allow us to develop some truly innovative technological solutions to an issue that is unfortunately not only happening more regularly but is also growing in complexity."

Additional Resources

Learn more at http://www.ensighten.com or email info@ensighten.com

About Ensighten

Ensighten provides security technology to prevent client-side website data theft to the world's leading brands, protecting billions of online transactions. Through its cloud-based security platform, Ensighten continuously analyzes and secures online content at the point where it is most vulnerable: in the customer's browser. Ensighten threat intelligence focuses on client-side website attacks to provide the most comprehensive protection against web skimming, JavaScript Injection, malicious adware and emerging methods.

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Ensighten Launches Client-Side Threat Intelligence Initiative and Invests in Machine Learning - WFMZ Allentown

STMicroelectronicshas released a free STM32 software function pack that lets users quickly build, train, and deployintelligent edge devices for industrial condition monitoringusing a microcontroller Discovery kit.

Developed in conjunction with machine-learning expert and ST Authorized Partner Cartesiam, theFP-AI-NANOEDG1 software packcontains all the necessary drivers, middleware, documentation, and sample code to capture sensor data, integrate, and run Cartesiams NanoEdge libraries. Users without specialist AI skills can quickly create and export custom machine-learning libraries for their applications using Cartesiams NanoEdge AI Studio tool running on a Windows 10 or Ubuntu PC. The function pack simplifies complete prototyping and validation free of charge on STM32 development boards, before deploying on customer hardware where standard Cartesiam fees apply.

The straightforward methodology established with Cartesiam uses industrial-grade sensors on-board a Discovery kit such as theSTM32L562E-DKto capture vibration data from the monitored equipment both in normal operating modes and under induced abnormal conditions. Software to configure and acquire sensor data is included in the function pack. NanoEdge AI Studio analyzes the benchmark data and selects pre-compiled algorithms from over 500 million possible combinations to create optimized libraries for training and inference. The function-pack software provides stubs for the libraries that can be easily replaced for simple embedding in the application. Once deployed, the device can learn the normal pattern of the operating mode locally during the initial installation phase as well as during the lifetime of the equipment, as the function pack permits switching between learning and monitoring modes.

Using the Discovery kit to acquire data, generate, train, and monitor the solution, leveraging free tools and software, and the support of theSTM32 ecosystem, developers can quickly create a proof-of-concept model at low cost and easily port the application to other STM32 microcontrollers. As an intelligent edge device, unlike alternatives that rely on AI in the cloud, the solution allows equipment owners greater control over potentially sensitive information by processing machine data on the local device.

The FP-AI-NANOEDG1 function pack is available now atwww.st.com, free of charge.

The STM32L562E-DK Discovery kit contains anSTM32L562QEI6QUultra-low-power microcontroller, an iNEMO 3D accelerometer and 3D gyroscope, as well as two MEMS microphones, a 240240 color TFT-LCD module, and on-board STLINK-V3E debugger/programmer. The budgetary price for the Discovery kit is $76.00, and it is available fromwww.st.comor distributors.

For further information, visitwww.st.com

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STMicroelectronics Releases STM32 Condition-Monitoring Function Pack Leveraging Tools from Cartesiam for Simplified Machine Learning - ELE Times

AUSTIN, Texas, Aug. 5, 2020 /PRNewswire/ -- Blacklight Solutions, an applied analytics company based in Texas, introduced today a simplified business analytics platform that allows small to mid-market businesses to implement artificial intelligence and machine learning with code free transformation, aggregation, blending and mixing of multiple data sources. Blacklight software empowers companies to increase efficiency by using machine learning and artificial intelligence for business processes with a team of experts guiding this metamorphosis.

"Small and mid-size firms need a simpler way to leverage these technologies for growth in the way large enterprises have." said Chance Coble, Blacklight Solutions CEO. "We are thrilled to bring an easy pay-as-you-go solution along with the expertise to guide them and help them succeed."

Blacklight Solutions believes that now more than ever companies need business analytics solutions that can increase sales, enhance productivity, and improve risk control. Blacklight software gives small to mid-market businesses an opportunity to implement the latest technology and create insightful digital products without requiring a dedicated team or familiarity with coding languages. Blacklight Solutions provides each client with a team of experts to help guide their journey in becoming evidence-based decision makers.

Capabilities and Benefits for Users

Blacklight is a cloud-based system that is built to scale with your business as it grows. It is the simplest way to create business analytics solutions that users can then sell to their customers. Users have the added ability to create dashboards and embed them in client facing portals. Additionally, users are enabled to grow and improve cash flow by creating data products that their customers can subscribe to resulting in generated revenue. Blacklight software also features an alerting system that notifies designated users when changes in data or anomalies occur.

"Blacklight brought the strategy, expertise and software that made analytics a solution for us to achieve new business objectives and grow sales," said Deren Koldwyn, CEO, Avannis, Blacklight Solutions client.

Blacklight software brings the full power of business analytics to companies that are looking for digital transformations and want to move fast. Blacklight Solutions is the only full-service solution that provides empowering software combined with the insight and strategy necessary for impactful analytics implementations. To learn more about Blacklight Solutions' offerings visit http://www.blacklightsolutions.com.

About Blacklight Solutions

Blacklight Solutions is an analytics firm focused on helping mid-market companies accelerate their growth. Founded in 2009, Blacklight Solutions has spent over a decade helping organizations solve business problems by putting their data to work to generate revenue, increase efficiency and improve customer relationships.

Media Contact:

Bailey Steinhauser979.966.8170[emailprotected]

SOURCE Blacklight Solutions

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Blacklight Solutions Unveils Software to Simplify Business Analytics with AI and Machine Learning - PRNewswire

There are many photos of Tom Hanks, but none like the images of the leading everyman shown at the Black Hat computer security conference Wednesday: They were made by machine-learning algorithms, not a camera.

Philip Tully, a data scientist at security company FireEye, generated the hoax Hankses to test how easily open-source software from artificial intelligence labs could be adapted to misinformation campaigns. His conclusion: People with not a lot of experience can take these machine-learning models and do pretty powerful things with them, he says.

Seen at full resolution, FireEyes fake Hanks images have flaws like unnatural neck folds and skin textures. But they accurately reproduce the familiar details of the actors face like his brow furrows and green-gray eyes, which gaze cooly at the viewer. At the scale of a social network thumbnail, the AI-made images could easily pass as real.

To make them, Tully needed only to gather a few hundred images of Hanks online and spend less than $100 to tune open-source face-generation software to his chosen subject. Armed with the tweaked software, he cranks out Hanks. Tully also used other open-source AI software to attempt to mimic the actors voice from three YouTube clips, with less impressive results.

A deepfake of Hanks created by researchers at FireEye.

By demonstrating just how cheaply and easily a person can generate passable fake photos, the FireEye project could add weight to concerns that online disinformation could be magnified by AI technology that generates passable images or speech. Those techniques and their output are often called deepfakes, a term taken from the name of a Reddit account that late in 2017 posted pornographic videos modified to include the faces of Hollywood actresses.

Most deepfakes observed in the wilds of the internet are low quality and created for pornographic or entertainment purposes. So far, the best-documented malicious use of deepfakes is harassment of women. Corporate projects or media productions can create slicker output, including videos, on bigger budgets. FireEyes researchers wanted to show how someone could piggyback on sophisticated AI research with minimal resources or AI expertise. Members of Congress from both parties have raised concerns that deepfakes could be bent for political interference.

Tullys deepfake experiments took advantage of the way academic and corporate AI research groups openly publish their latest advances and often release their code. He used a technique known as fine-tuning in which a machine-learning model built at great expense with a large data set of examples is adapted to a specific task with a much smaller pool of examples.

To make the fake Hanks, Tully adapted a face-generation model released by Nvidia last year. The chip company made its software by processing millions of example faces over several days on a cluster of powerful graphics processors. Tully adapted it into a Hanks-generator in less than a day on a single graphics processor rented in the cloud. Separately, he cloned Hanks voice in minutes using only his laptop, three 30-second audio clips, and a grad student's open-source recreation of a Google voice-synthesis project.

A "deepfake" audio clip of Tom Hanks created by FireEye.

As competition among AI labs drives further advancesand those results are sharedsuch projects will get more and more convincing, he says. If this continues, there could be negative consequences for society at large, Tully says. He previously worked with an intern to show that AI text-generation software could create content similar to that produced by Russias Internet Research Agency, which attempted to manipulate the 2016 presidential election.

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Cheap, Easy Deepfakes Are Getting Closer to the Real Thing - WIRED

Topics The AI & Machine Learning Imperative

The AI & Machine Learning Imperative offers new insights from leading academics and practitioners in data science and artificial intelligence. The Executive Guide, published as a series over three weeks, explores how managers and companies can overcome challenges and identify opportunities by assembling the right talent, stepping up their own leadership, and reshaping organizational strategy.

Leading organizations recognize the potential for artificial intelligence and machine learning to transform work and society. The technologies offer companies strategic new opportunities and integrate into a range of business processes customer service, operations, prediction, and decision-making in scalable, adaptable ways.

As with other major waves of technology, AI requires organizations and managers to shed old ways of thinking and grow with new skills and capabilities. The AI & Machine Learning Imperative, an Executive Guide from MIT SMR, offers new insights from leading academics and practitioners in data science and AI. The guide explores how managers and companies can overcome challenges and identify opportunities across three key pillars: talent, leadership, and organizational strategy.

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The series launches Aug. 3, and summaries of the upcoming articles are included below. Sign up to be reminded when new articles launch in the series, and in the meantime, explore our recent library of AI and machine learning articles.

In order to achieve the ultimate strategic goals of AI investment, organizations must broaden their sights beyond creating augmented intelligence tools for limited tasks. To prepare for the next phase of artificial intelligence, leaders must prioritize assembling the right talent pipeline and technology infrastructure.

Recent technical advances in AI and machine learning offer genuine productivity returns to organizations. Nevertheless, finding and enabling talented individuals to succeed in engineering these kinds of systems can be a daunting challenge. Leading a successful AI-enabled workforce requires key hiring, training, and risk management considerations.

AI is no regular technology, so AI strategy needs to be approached differently than regular technology strategy. A purposeful approach is built on three foundations: a robust and reliable technology infrastructure, a specific focus on new business models, and a thoughtful approach to ethics. Available Aug. 10.

CFOs who take ownership of AI technology position themselves to lead an organization of the future. While AI is likely to impact business practices dramatically in the future across the C-suite, its already having an impact today and the time for CFOs to step up to AI leadership is now. Available Aug. 12.

To remain relevant and resilient, companies and leaders must strive to build business models in a way that ensures three key components are working together: AI that enables and powers a centralized data lake of enterprise data, a marketplace of sellers and partners that make individualized offers based on the intelligence of the data collected and powered by AI, and a SaaS platform that is essential for users. Available Aug. 17.

Acquiring the right AI technology and producing results, while critical, arent enough. To gain value from AI, organizations need to focus on managing the gaps in skills and processes that impact people and teams within the organization. Available Aug. 19.

The AI & Machine Learning Imperative offers new insights from leading academics and practitioners in data science and artificial intelligence. The Executive Guide, published as a series over three weeks, explores how managers and companies can overcome challenges and identify opportunities by assembling the right talent, stepping up their own leadership, and reshaping organizational strategy.

Ally MacDonald (@allymacdonald) is a senior editor at MIT Sloan Management Review.

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Introducing The AI & Machine Learning Imperative - MIT Sloan

AUSTIN, Texas, Aug. 5, 2020 /PRNewswire/ -- Blacklight Solutions, an applied analytics company based in Texas, introduced today a simplified business analytics platform that allows small to mid-market businesses to implement artificial intelligence and machine learning with code free transformation, aggregation, blending and mixing of multiple data sources. Blacklight software empowers companies to increase efficiency by using machine learning and artificial intelligence for business processes with a team of experts guiding this metamorphosis.

"Small and mid-size firms need a simpler way to leverage these technologies for growth in the way large enterprises have." said Chance Coble, Blacklight Solutions CEO. "We are thrilled to bring an easy pay-as-you-go solution along with the expertise to guide them and help them succeed."

Blacklight Solutions believes that now more than ever companies need business analytics solutions that can increase sales, enhance productivity, and improve risk control. Blacklight software gives small to mid-market businesses an opportunity to implement the latest technology and create insightful digital products without requiring a dedicated team or familiarity with coding languages. Blacklight Solutions provides each client with a team of experts to help guide their journey in becoming evidence-based decision makers.

Capabilities and Benefits for Users

Blacklight is a cloud-based system that is built to scale with your business as it grows. It is the simplest way to create business analytics solutions that users can then sell to their customers. Users have the added ability to create dashboards and embed them in client facing portals. Additionally, users are enabled to grow and improve cash flow by creating data products that their customers can subscribe to resulting in generated revenue. Blacklight software also features an alerting system that notifies designated users when changes in data or anomalies occur.

"Blacklight brought the strategy, expertise and software that made analytics a solution for us to achieve new business objectives and grow sales," said Deren Koldwyn, CEO, Avannis, Blacklight Solutions client.

Blacklight software brings the full power of business analytics to companies that are looking for digital transformations and want to move fast. Blacklight Solutions is the only full-service solution that provides empowering software combined with the insight and strategy necessary for impactful analytics implementations. To learn more about Blacklight Solutions' offerings visit http://www.blacklightsolutions.com.

About Blacklight Solutions

Blacklight Solutions is an analytics firm focused on helping mid-market companies accelerate their growth. Founded in 2009, Blacklight Solutions has spent over a decade helping organizations solve business problems by putting their data to work to generate revenue, increase efficiency and improve customer relationships.

Media Contact:

Bailey Steinhauser979.966.8170244826@email4pr.com

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Blacklight Solutions Unveils Software to Simplify Business Analytics with AI and Machine Learning - El Paso Inc.

The context: Studies show that when people and AI systems work together, they can outperform either one acting alone. Medical diagnostic systems are often checked over by human doctors, and content moderation systems filter what they can before requiring human assistance. But algorithms are rarely designed to optimize for this AI-to-human handover. If they were, the AI system would only defer to its human counterpart if the person could actually make a better decision.

The research: Researchers at MITs Computer Science and AI Laboratory (CSAIL) have now developed an AI system to do this kind of optimization based on strengths and weaknesses of the human collaborator. It uses two separate machine-learning models; one makes the actual decision, whether thats diagnosing a patient or removing a social media post, and one predicts whether the AI or human is the better decision maker.

The latter model, which the researchers call the rejector, iteratively improves its predictions based on each decision makers track record over time. It can also take into account factors beyond performance, including a persons time constraints or a doctors access to sensitive patient information not available to the AI system.

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AI is learning when it should and shouldnt defer to a human - MIT Technology Review

Harqen's AI platform analyses language to determine a candidate's suitability for a role, potentially making it less prone to bias than video-based recruitment technology.

Artificial-intelligence-based hiring tools are already transforming the recruitment process by allowing businesses to vastly speed up the time it takes to identify top talent. With algorithms able to scour applications databases in the fraction of a time it would take a human hiring manager, AI-assisted hiring has the potential not only to give precious time back to businesses, but also draw in candidates from wider and more diverse talent pools.

AI-assisted hiring is also posited as a potential solution for reducing human bias whether subconscious or otherwise in the hiring process.

SEE: Robotic process automation: A cheat sheet (free PDF) (TechRepublic)

US company Harqen has been offering hiring technologies to some of the world's biggest companies for years, partnering with the likes of Walmart, FedEx and American Airlines to streamline and improve their hiring processes. Originating as an on-demand interviewing provider, the company has now expanded into AI with a new platform that it says offers a more dependable and bias-free means of matching employers with employees.

The solution, simply called the Harqen Machine Learning Platform, analyses candidate's answers to interview questions and assesses the type of words and language used in their responses. According to Harqen, this allows it to put together a profile of psychological traits that can be used to help determine a candidate's suitability for a role.

Combined with a resume analysis, which provides a more straightforward determiner of whether a candidate's professional and educational background fits with the requirements of the job, Harqen says its machine-learning platform is capable of making the same hiring decision as human recruiters 95% of the time. In one campaign that assessed approximately 3,500 job applications with "a very large US diagnostic firm" in early 2020, Harqen's machine-learning platform successfully predicted 2,193 of the candidate applications that were accepted, and 1,292 that were declined.

Key to Harqen's offering is what the company's chief technology officer Mark Unak describes as the platform's linguistic analysis, which can identify word clusters that are specific to certain job types but also offers a personality analysis based on the so-called "big five" traits, also known as the OCEAN model (openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism), which can help hiring managers determine a candidate's enthusiasm for the position.

"We have a dictionary of terms where most positive words are ranked as a +5 and most negative words are ranked as a -5, so we can determine how enthusiastic you are in the answers that you're giving," Unak tells TechRepublic.

"We can also use a linguistic analysis to analyse the grammar," he adds, noting that about 60% of our vocabulary consists of just 80 words. Those are the pronouns, the propositions, the articles and the intransigent verbs. "The remaining 10,000 words in the English language fill in that 40%. By the analysis of how you use that, we can get a psychological trait analysis."

Harqen's machine-learning tool analyses word clusters to help determine candidates' personality traits, such as enthusiasm.

Source: Harqen.ai

According to Unak, using a machine-learning system that determines a candidate's suitability based on linguistic analysis is a more accurate and impartial method than those that rely on facial-scanning or vocal-inflection algorithms. Such machine-learning techniques within hiring are on the rise and are increasingly being adopted by major companies around the world.

"That's kind of problematic," says Unak. "Not everybody expresses emotions in the same way, with the same facial expressions, and not everybody expresses the same emotion that's expected. Different cultures and different races might have different problems in expressing those facial expressions and having the computer recognise them."

SEE:Diversity and Inclusion policy (TechRepublic Premium)

By only analysing the linguistic content that has been transcribed from recorded interviews, Harqen's algorithm never factors in appearance, facial expressions, or other self-reported personality traits that could be unreliable. Unak says the company will also retrain its models on a regular basis as new data comes in, which will help ensure that algorithms don't get stuck in their old ways if candidates begin giving new answers to questions that are equally relevant.

"If our customer evolves and they start to hire people who are either more diverse, or come up with different answers to the questions that are just as relevant, our models will pick that up," Unak adds.

Diversity whether based on gender, race, age or otherwise has been show to play a significant role in the success or failure of workplace productivity and collaboration. Whether AI-based hiring tools can help here remains to be seen, and ultimately depends on whether they can be implemented in a fair and impartial way.

Beyond diversity, Harqen is exploring how its machine-learning tool could help businesses get the best return on investment form their hiring choices. The magic word here is delayed gratification: the ability to accurately identify employees who can resist the temptation for immediate rewards and instead persevere for an even greater payoff in the future.

"It's grit, it's persistence, it's the ability to imagine a future and it's the ability to develop and execute a plan to get there," says Unak. "Isn't that what hope and delayed gratification mean? I hope for a better future, I can imagine it, my hope is realistic and that there's a plan or a way to get there, and I'm going to work towards it."

Learn the latest news and best practices about data science, big data analytics, and artificial intelligence. Delivered Mondays

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Job interviews: Recruiters are using artificial intelligence to analyse what you say to find the right hire - TechRepublic

A survey of over 19,000 data professionals showed that nearly 2/3rds of respondents said they analyze data to influence product/business decisions. Only 1/4 of respondents said they do research to advance the state of the art of machine learning. Different data roles have different work activity profiles with Data Scientists engaging in more different work activities than other data professionals.

We know that data professionals, when working on data science and machine learning projects, spend their time on a variety of different activities (e.g., gathering data, analyzing data, communicating to stakeholders) to complete those projects. Todays post will focus on the broad work activities (or projects) that make up their roles at work, including Build prototypes to explore applying machine learning to new areas and Analyze and understand data to influence product or business decisions. Toward that end, I will use the data from the recent Kaggle survey of over 19,000 data professionals in which respondents were asked a variety of questions about their analytics practices, including their job title, work experience and the tools and products they use.

The survey respondents were asked to Select any activities that make up an important part of your role at work: (Select all that apply). On average respondents indicated that two (median) of the activities make up on important part of their role. The entire list of activities (shown in Figure 1) were:

Figure 1. Activities that Make Up Important Parts of Data Professionals Role

The The top work activity was somewhat practical in nature, helping the company improve how it runs the business: analyzing data to influence products and decisions. The work activity with the lowest endorsement was more theoretical in nature: doing research that advances the state of the art of machine learning.

Next, I examined if there were differences across different data roles (as indicated by respondents job title) with respect to work activities. I looked at 5 different job title for this analysis. The results revealed a couple of interesting findings (See Figure 2):

First, respondents who self-identified as Data Scientists, on average, indicated that they are involved in 3 (median) activities at work compared to the other respondents who are involved in 2 job activities.

Second, we see that the profile of work activities varies greatly across different data roles. While many of the respondents indicated that analysis and understanding of data to influence products/decisions was the top activity for them, a top activity for Research Scientists was doing research that advances the state of the art of machine learning. Additionally, the top activity for Data Engineers was building and/or running the data infrastructure.

Figure 2. Typical work activities vary across different data roles.

The top work activity for data professional roles appears to be very practical and necessary to run day-to-day business operations. These top work activities included influencing business decisions, building prototypes to expand machine learning to new areas and improving ML models. The bottom activity was more about long-term understanding of machine learning reflected in conducting research to advance the state of the art of machine learning.

Different data roles possess different activity profiles. Top work activities tend to be associated with the skill sets of different data roles. Building/Running data infrastructure was the top activity for Data Engineers; doing research to advance the field of machine learning was a top activity for Research Scientists.These results are not surprising as we know that different data professionals have different skill sets. In prior research, I found that data professionals who self-identified as Researchers have a strong math/statistics/research skill set. Developers, on the other hand, have strong programming/technology skills. And data professionals who were Domain Experts have strong business-domain knowledge. Data science and machine learning work really is a team sport. Getting data teams with members who have complementary skill sets will likely improve the success rate of data science projects.

Remember that data professionals have their unique skill set that makes them a better fit for some data roles than others.When applying for data-related positions, it might be useful to look at the type of work activities for which you have experience (or are competent) and apply for the positions with corresponding job titles. For example, if you are proficient in running a data infrastructure, you might consider focusing on Data Engineer jobs. If you have a strong skill set related to research and statistics, you might be more likely to get a call back when applying for Research Scientist positions.

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Who Does the Machine Learning and Data Science Work? - Customer Think

With evolving technologies, intelligent automation has become a top priority for many executives in 2020. Forrester predicts the industry will continue to grow from $250 million in 2016 to $12 billion in 2023. With more companies identifying and implementation the Artificial Intelligence (AI) and Machine Learning (ML), there is seen a gradual reshaping of the enterprise.

Industries across the globe integrate AI and ML with businesses to enable swift changes to key processes like marketing, customer relationships and management, product development, production and distribution, quality check, order fulfilment, resource management, and much more. AI includes a wide range of technologies such as machine learning, deep learning (DL), optical character recognition (OCR), natural language processing (NLP), voice recognition, and so on, which creates intelligent automation for organizations across multiple industrial domains when combined with robotics.

Let us see how some of these technologies help industries globally to implement automation.

Machine learning has recently been applied to detect anomalies in manufacturing processes. Using machine learning, health monitoring of the equipment can be automated where the specialties of the sensor devices data like vibrations, sound, temperature, etc. from the collected data can be learned through training.

This is useful to identify early wear and tear of equipment and avoid catastrophic damage. It can catch the smallest flaw that the human eye may miss. Techniques can be selected depending on the type of attributes required to extract the features and based on the features various machine learning algorithms can be applied to detect the anomalies.

One of the main tasks of any machine learning algorithm in the self-driving car is a continuous rendering of the surrounding environment and the prediction of possible changes to those surroundings. It is essential for autonomous cars to recognize objects or pedestrians on the road, irrespective whether it is day or night. For the success of autonomous cars, automobile companies integrate advanced driver assist systems (ADAS) with thermal imaging.

By executing deep learning algorithms on the image data set that are captured by thermal cameras, it is possible to identify pedestrians in any weather condition. It can cover a larger or small part of the image based on distance. There are few deep learning algorithms like Fast R-CNN or YOLO that can help achieve this automation making autonomous cars safer and efficient on roads.

OCR is another technology which uses deep learning to recognize characters. It is of great use in manufacturing to automate processes which are subject to human errors due to fatigue or casual behavior. These activities include verifications of lot code, batch code, expiry date etc. Various CNN architectures like LeNet, Alexnet etc. can be used for this automation and it can also be customized to achieve the desired accuracy.

Loaning money is a huge business for financial institutions. The value and approval of the loans is entirely based on how likely an individual or business will be able to repay. Determining creditworthiness is most important decision for this business to succeed. Along with credit score various other parameters are considered for making such decisions which makes the whole process very complex and time consuming.

To save on time and accelerate the process, trained machine learning algorithms can be used to predict and classify the creditworthiness of the applicant. This can simplify the classification of applicants and improve decision making for loan sanction.

AI and ML is creating a new vision of machine-human collaboration and taking businesses to new levels. Machine learning helps organizations across various industrial domains to develop intelligent solutions based on proprietary or open source algorithms/frameworks that processes data and runs sophisticated algorithms on cloud and edge. Machine Learning models can be built, trained, validated, optimized, deployed and tested using latest tools and technologies. This ensures faster decision making, increased productivity, business process automation, and faster anomaly detection for the businesses.

Kaumil Desai is associated withVOLANSYSas a Delivery Manager past 3 years. He has vast experience in product development, Machine Learning on edge, complex algorithms design & development for various industries including Industrial Automation, Electrical safety, Telecom, etc.

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Artificial Intelligence and Machine Learning Path to Intelligent Automation - Embedded Computing Design