Monthly Archives: March 2022

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Pony.ai develops autonomous robotaxi and trucking technology. | Source: Pony.ai

Pony.ai agreed to issue a recall on some versions of its autonomous driving software following a car accident that occurred in October 2021. The company recalled the now repaired vehicles because they used software associated with the crash.

During the October accident, a Pony.ai vehicle hit a street sign on a median during a turn in Fremont, California while in autonomous mode. No one was injured in the incident, but it prompted the California Department of Motor Vehicles (DMV) to suspend the companys driverless testing permit.

While there are plenty of crashes reported involving autonomous vehicles, this one stood out because the vehicle was operating in autonomous mode and didnt involve any other vehicles.

According to reporting from Reuters, Pony.ai said that the crash occurred less than 2.5 seconds after the autonomous driving system shut down. The National Highway Traffic Safety Administration (NHTSA) told the company that it believed the software had a safety defect, which lead to the requested recall.

Pony.ai complied with the request and updated the software in the three vehicles. According to the agency, this is the first recall of an autonomous driving system.

Pony.ai received its autonomous driving permit from the California DMV six months before it suspended it. At the time, Pony.ai was the eighth company to receive the permit, after Apollo, AutoX, Cruise, Nuro, Waymo, WeRide and Zoox.

Earlier this month, Cruise and Waymo received Drivered Deployment permits from the California Public Utilities Commission (CPUC). The permit allows the companies to charge customers fares for their services. The permits require both companies to have a safety driver present in the vehicles at all times.

This week, Pony.ai announced that it is now valued at $8.5 billion dollars after the first close of the companys Series D funding round. The company ended its last funding round at a $5.3 billion valuation.

Pony.ai plans to use the funding to further augment hiring and enter more strategic partnerships. It also plans to invest in research and development, including global testing of its fleet of robotaxis and robotrucking and moving closer to mass production and commercial deployment of its fleet.

The company did not disclose details on the amount raised in the funding round so far, but plans to announce totals when the full round closes.

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Pony.ai agrees to recall 3 of its autonomous vehicles - The Robot Report

By Xiaoman Hu

Encouraging women in AI has never been more urgent. A study by the World Economic Forum noted a gender disparity of 78 percent male versus 22 percent female in AI and data science. This disparity isnt just a challenge within the workforce. It reflects a highly nuanced issue that goes beyond any single workplace and if not addressed will have highly negative implications for society.

We have seen a lot of work to encourage girls and women to become interested in STEM and address gaps in digital skills at an earlier age than in the past. Yet now, there appears to be less effort to support women as they transition from higher education into a sustainable career in tech. This is a challenge for the industry. But the real problem is that as AI becomes ubiquitous in daily life, without a technology workforce that accurately reflects the structure of society, AI-based decisions are constrained by the limited societal and cultural biases of their designers. The impact of such homogeneity in AI decisions and bias has already been seen in examples such as the automation of credit card and mortgage applications, to resume screening and other areas.

The industry challenge is not due to a lack of skills. Research from the Turing Institute suggests women are trailing behind men with industry-relevant skills such as computer science, data preparation and exploration, general-purpose computing, databases, big data, machine learning, statistics, and mathematics. Yet much of this is not due to formal skills, but rather confidence by women in stating these abilities during recruitment and in the workplace. In the tech world where technical skills are needed, soft skills are sometimes dismissed but in order to move forward, there needs to be a greater focus on leadership and mentorship to build confidence and encourage a more diverse workforce. We say that stereotypes must be combatted from a young age yet a gap remains. For example, within the tech sector, women generally have higher levels of formal education than their male counterparts yet academic citations are fewer suggesting there is a lack of confidence in sharing academic knowledge. The Turing Institute finds that only 20 percent of UK data and AI researchers on Google Scholar are women. Of the 45 researchers with more than 10,000 citations, only five were women.

When I say that women need to have mentors and role models, I write from firsthand experience. It was only after winning a mathematics modeling competition in university that I considered a related career. This inspired me to write a blog on machine learning algorithms. The easy-to-understand method employed helped the blog garner over 5 million views, and eventually led to a career in programming. When I became a programmer and found myself working as the only woman in a room of men typically 10-15 years older, I struggled to relate and realized the need for a community of like-minded people.

In April 2020 I started to manage operations for MindSpore, an AI framework developed by Huawei, just as it became open source. MindSpore is Huaweis alternative AI framework to Googles TensorFlow and Facebooks PyTorch with comparable capabilities but 20 percetn fewer lines of code. Launched in September 2019, it is endorsed by major universities including Peking University, University of Edinburgh, and Imperial College. Today, MindSpore boasts over 1.3 million downloads and an interactive community indicated by over 19,000 issues, over 52,000 pull requests, and over 16,000 stars (the equivalent of a like among developers).

In 2021, open-source component downloads grew 73 percent YOY. With the rapid growth in the global adoption of open source technology, diversity in open source communities is also increasing. The MindSpore Women in Tech Community emphasizes seminar-like gatherings which provide women a safe space to discuss the challenges they face in the workplace. Mentoring is important. For example, in 2020, when the community was just in its infancy, a student at one of our events explained she was getting good grades but was worried about a career in programming. She sought advice from more senior programmers and tech leaders. By the time she graduated she had no need to worry and was able to choose from one of several offers. Not only did she feel more confident but was able to give back to the community by sharing her experience with new students, those who were now in the position she had been the previous year. It is experiences like this that will keep women in tech. When they stay, tech also benefits.

But encouraging women isnt simply about creating diversity within the industry to enable greater gender balance. The benefits stretch beyond the sector and into the societal benefits. With the digitalization of many traditional sectors, the pervasive nature of AI demands that it not only provides efficiency but is also inclusive. It is only by broadening the pool of talent that we can avoid data-led decisions skewed by bias. Establishing communities that actively foster participation and diverse voices is an important step.

Bias in AI starts with the initial formulation of problems. The questions are naturally constrained by the experiences of the designers and programmers. This in turn impacts the quality of the data and the way it is handled. So what will be the societal impact if there is not greater diversity?

So in conclusion, now that our lives are digitally-driven, we must ensure that women can enjoy the benefit of technology for generations to come rather than be negatively impacted.

About the author

Xiaoman Hu is the Director of Operations at MindSpore Community

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Encouraging women in tech is essential to protect society against AI bias - TechTalks

Editor's note: This article is the latest in a series that looks into the ways supply chains, warehouses and manufacturing facilities are investing in technology. Here's the previous story.

Artificial intelligence isn't yet perfect.The technology has led to some major business flubs (like that time Microsoft's AI chatbot "learned" to be racist, sexist and anti-Semetic in 2016). But AI is not going away in the supply chain. In fact, it's prevalence is expected to grow.

In the 2021 MHI Annual Industry Report, 17% of respondents said they use AI already, and another 45% predicted they'll use it in five years. The survey of more than 1,000 supply chain professionals worldwide also found that 25% plan to make investments in AI products in the next three years.

"[AI] is very complex, but what we can use is very, very simple. People don't need to have a deep understanding of the algorithms," said Ben Lynch, director of business data analytics at DHL Supply Chain. "I never thought it would be able to move as quickly as it has, and it's only going to get better."

In the last five years, AI has shifted the relationship DHL has with its customers.

The company went from giving customers information for something that's already happened to "something that's a bit more predictive," Lynch said. "Through AI, machine learning and data availability, now we can give them insights into not just what's happened but what's going to happen."

That transition has been fueled by sophisticated algorithms that can handle the sheer amount of data collected.

"Every two years, we are generating as much data that has ever been created. By 2023, we'll have twice as much data in the world. Because of this, there's been a big need for technology to help support this data," Lynch said.

AI is also enabling the advancement of other kinds of technologies in the supply chain, like robotics, said Thomas Evans, robotics chief technology officer at Honeywell.

% of respondents who plan to invest in products and services over the next three years

"The complexity and the way-quicker access to AI through third party providers, and also the ability to build AI platforms and deploy them, is a drastic change and asset to supply chain logistics," Evans said. "It's only going to get more advanced as we harness more and more data."

AI isn't a panacea for business problems, though. It has experienced growing pains.

More recently than Microsoft's chatbot ordeal, the online real estate company Zillow shutdown Zillow Offers, an AI-fueled home buying and flipper service,because the company bought houses for higher than they could resell. The company took a $304 million inventory write down in the third quarter of this 2021.

But in the right use cases, AI is sophisticated, effective and already paying off.

A McKinsey report found that, for early adopters, AI-enabled supply chain management improved logistics costs by 15%, inventory levels by 35%, and service levels by 65%, compared to "slower-moving competitors."

The biggest gap Lynch sees to further adoption is simplifying data and "getting data and business to speak the same language," he said.

"As the amount of technology and digitalization increased and opened up more and more data, the struggle we've had is, how do you take this data and transform it into something that makes more sense for the operator?" he posed.

That will help drive decision-making on the operator end.

Lynch doesn't expect the flow of data to stop, either. He said he thinks it will grow along with the booming e-commerce landscape, fueled by all the consumer data drawn from that online activity.

"Now that we can understand what a consumer is going to buy next week, how do we set up our warehouses?" he said. "The technology get better and better and we'll get a really good sense at the consumer level as opposed to the aggregate supply chain level."

Wider roll out of AI will also depend on robust cybersecurity, Evans said. If data can't be captured, stored and shared with company partners securely, it becomes a liability instead of a boost.

"That's why we're seeing some of the vulnerabilities and ransomware concerns bigger businesses are having," he said, adding that, right now, he's working on a commercial product release and said that 80% to 90% of his engineers are focused on product security.

As the value of data to run AI systems become more valuable to businesses, they become valuable to bad actors, too. "Over time it becomes more of a target," Evans said.

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Here to stay: Supply chains gear up for investments in AI - Supply Chain Dive

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Virtually every enterprise decision-maker across the economic spectrum knows by now that artificial intelligence (AI) is the wave of the future. Yes, AI has its challenges and its ultimate contribution to the business model is still largely unknown, but at this point its not a matter of whether to deploy AI but how.

For most of the C-suite, even those running the IT side of the house, AI is still a mystery. The basic idea is simple enough software that can ingest data and make changes in response to that data but the details surrounding its components, implementation, integration and ultimate purpose are a bit more complicated. AI isnt merely a new generation of technology that can be provisioned and deployed to serve a specific function; it represents a fundamental change in the way we interact with the digital universe.

So even as the front office is saying yes to AI projects left and right, it wouldnt hurt to gain a more thorough understanding of the technology to ensure it is being employed productively.

One of the first things busy executives should do is gain a clear understanding of AI terms and the various development paths currently underway, says Mateusz Lach, AI and digital business consultant at Nexocode. After all, its difficult to push AI into the workplace if you dont understand the difference between AI, ML, DL and traditional software. At the same time, you should have a basic working knowledge of the various learning models being employed (reinforcement, supervised, model-based ), as well as ways AI is used (natural language processing, neural networking, predictive analysis, etc.)

With this foundation in hand, it becomes easier to see how the technology can be applied to specific operational challenges. And perhaps most importantly, understanding the role of data in the AI model, and how quality data is of prime importance, will go a long way toward making the right decisions as to where, when and how to employ AI.

It should also help to understand where the significant challenges lie in AI deployment, and what those challenges are. Tech consultant Neil Raden argues that the toughest going lies in the last mile of any given project, where AI must finally prove that it can solve problems and enhance value. This requires the development of effective means of measurement and calibration, preferably with the capability to place results in multiple contexts given that success can be defined in different ways by different groups. Fortunately, the more experience you gain with AI the more you will be able to automate these steps, and this should lessen many of the problems associated with the last mile.

Creating the actual AI models is best left to the line-of-business workers and data scientists who know what needs to be done and how to do it, but its still important for the higher ups to understand some of the key design principles and capabilities that differentiate successful models from failures. Andrew Clark, CTO at AI governance firm Monitaur, says models should be designed around three key principals:

As well, models should exhibit a number of other important qualities, such as reperformance (aka, consistency), interpretability (the ability to be understood by non-experts), and a high degree of deployment maturity, preferably using standard processes and governance rules.

Like any enterprise initiative, the executive view of AI should center on maximizing reward and minimizing risk. A recent article from PwC in the Harvard Business Review highlights some ways this can be done, starting with the creation of a set of ethical principles to act as a north star for AI development and utilization. Equally important is establishing clear lines of ownership over each project, as well as building a detailed review and approval process at multiple stages of the AI lifecycle. But executives should guard against letting these safeguards become stagnant, since both the economic conditions and regulatory requirements governing the use of AI will likely be highly dynamic for some time.

Above all, enterprise executives should strive for flexibility in their AI strategies. Like any business resource, AI must prove itself worthy of trust, which means it should not be released into the data environment until its performance can be assured and even then, never in a way that cannot be undone without painful consequences to the business model.

Yes, the pressure to push AI into production environments is strong and growing stronger, but wiser heads should know that the price of failure can be quite high, not just for the organization but individual careers as well.

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What business executives need to know about AI - VentureBeat

Signaling its ongoing commitment to collaboration in the areas of artificial intelligence and data science, the University of Florida is participating in an academic conference to address the potential of artificial intelligence, robotics and automation in agriculture.

The conference, titled Envisioning 2050 in the Southeast: AI-driven Innovations in Agriculture, is hosted March 9-11 by the Auburn University College of Agriculture and funded by the U.S. Department of Agriculture National Institute of Food and Agriculture.

Conference speakers include Hendrik Hamann, a distinguished research staff member and chief scientist for the future of climate in IBM Research; Mark Chaney, engineering manager of the automation delivery teams at Intelligent Solutions Group at John Deere; Steven Thomson, a national program leader with the USDA National Institute Food and Agriculture; and dozens more.

Speakers from academia, the federal government and industry will share their work in areas such as crop production, plant and animal breeding, climate, agricultural extension, pedagogy, food processing and supply chain, livestock management and more.

The Envisioning 2050 in the Southeast: AI-Driven Innovations in Agriculture conference will bring together academics, industry and stakeholders to share their expertise and develop a vision for the future, said Arthur Appel, interim associate dean of research for the Auburn College of Agriculture. Attendees will be able to learn about the depth and breadth of AI in agriculture from the experts who are making the promise of AI a reality.

Kati Migliaccio, co-organizer of the conference and chair of the Department of Agricultural and Biological Engineering at the University of Florida, said the timing of the conference is perfect.

This is an opportune time to host this conference focusing on AI in agriculture in the Southeast because of the resources invested in AI, the state of innovation of AI in agriculture and the critical need to adapt agriculture for current world challenges, including labor, nutrition, energy and climate, she said.

In November, the chief academic officers of the 14 member universities in the Southeastern Conference (SEC) announced formation of an artificial intelligence and data science consortium for workforce development, designed to grow opportunities in the fast-changing fields of AI and data science.

Believed to be the first athletics conference collaboration to have such a focus, the SEC Artificial Intelligence Consortium enables SEC universities to share educational resources, such as curricular materials, certificate and degree program structures, and online presentations of seminars and courses; promote faculty, staff, and student workshops and academic conferences such as todays event at Auburn; and seek joint partnerships with industry.

Joe Glover, provost and senior vice president for academic affairs at the University of Florida, which is leading the SEC-wide effort, said, AI is changing nearly every sector of society, and the SEC is uniquely positioned to engage students, faculty, and staff in one of the most transformational opportunities of our time. The combined strength of our institutions gives us the opportunity to advance in how we process the future of teaching and learning, research and economic development and how we can provide leadership at this critical moment when AI and data science are changing the way we think about small tasks and big questions.

The Auburn University office of communications and marketing and the SEC communications office contributed to this story.

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Universities meet to discuss future of AI and data science in agriculture - University of Florida

Theres no doubt: Machine learning and artificial intelligence are the hot specialties in IT right now but filling those jobs is proving to be tough.

In a September Gartner survey of over 400 global IT organizations, 64% of IT executives said that a lack of skilled talent was the biggest barrier to adoption of emerging technologies, compared with 4% the previous year.

Companies are looking for employees with specific training, skills and personal traits to fill positions STEM degrees, credentials specific to AI and machine learning, practical hands-on experience, and certain soft skills are all considered when deciding whether to hire a candidate.

The current push to find AI developers and engineers makes the shortage of candidates undeniable, and makes hiring particularly grueling.

In a November survey of over 2,500 human resources and engineering personnel by HackerEarth, a software company that helps organizations with their technical hiring needs, 30% of respondents said theyre expecting to hire more than 100 developers in the coming year.

With goals that ambitious, a significant portion of those hiring managers are so much in need for talent that theyre willing to compromise their standards. Nearly 35% of engineering managers said they would compromise on candidate quality to fill an opening quicker and nearly 24% of HR managers said the same.

According to the survey, AI and ML experts are in high demand this year, with demand exceeding supply.

What we have today is a rich tapestry of interrelated jobs or personas that all go into creating a data science or AI outcome in the enterprise.

Bradley Shimmin, chief analyst for AI platforms, analytics and data management, Omdia

Its a candidates market out there, said Vishwastam Shukla, chief technology officer for HackerEarth.

With companies from all industries looking to hire, larger organizations have the advantage of being able to offer bigger salaries and plusher benefits, he acknowledged. But hes seeing smaller employers and fast-growing startups put up a good fight for candidates.

One of the most popular tactics, Shukla said, is to actually inculcate a culture of learning and development within the organization.

The AI positions companies are looking to fill have become narrower and more specialized.

Job requirements vary wildly depending on a companys size, how mature they are, their data infrastructure and what kind of projects theyre working on, said Bradley Shimmin, chief analyst for AI platforms, analytics and data management at global analyst firm Omdia.

Five years ago, data scientist was considered the hottest job on the planet, and we were talking about data scientists as unicorns in that they possessed a number of very specific skills mathematical, statistical, business and communication, he said.

Companies realized early on that they couldnt operationalize with just a few jack-of-all-trades data scientists.

Trying to scale with them was impossible financially and that, coupled with the creation of MLOps platforms, really spawned a diversification for the job role and a slicing off of aspects of that job, said Shimmin. What we have today is a rich tapestry of interrelated jobs or personas that all go into creating a data science or AI outcome in the enterprise.

The job titles of AI and ML engineers and developers cover a wide variety of tasks and responsibilities, but theres a lot of overlap.

A necessary background for a potential employee starts with programming experience and a college degree.

Companies specifically look for:

And companies are hiring anywhere from basic entry-level positions to more advanced roles.

Were just hiring at all levels, said Valerie Junger, chief people officer at Quantcast, a technology company that focuses on AI-driven real-time advertising.

Machine learning engineers have to be fluent in Java, C++, Python, or similar development languages, and need anything from a masters degree to a Ph.D., depending on the role, she said.

Just having a general computer science degree isnt enough recruiters look for an applicant whos taken specific courses in AI and ML.

In the past, I would check that applicants had a math or STEM background only, said Rosaria Silipo, head of data science evangelism at KNIME, a data-analytics platform company. Now, with the proliferation of college programs and online courses, I check if they have any credentials specific to machine learning or data science.

The requirements for a machine learning engineer have changed, said Omdias Shimmin: All the platform players, Microsoft, Google, Amazon, and others are setting up certification programs.

You dont need to have a Ph.D. you can take whatever time it takes to prove certification as a machine learning engineer, or a data learning engineer, or as a machine learning specialist, and you can put that to work, he said. You can have a bachelors or a masters and still get into this area.

Pursuing specific credentials can lead to better jobs, or to a pay bump in a current position.

According to an October survey of over 3,000 data and AI professionals by learning company OReilly, 64% said they took part in training or obtained skills to build their professional skills, and 61% participated in training or earned certifications to get a salary increase or promotion.

And over a third of those polled dedicated more than 100 hours to training. Those survey participants reported an average salary increase of $11,000.

Entering competitions or hackathons can make a person stand out in a pool of prospective AI/ML candidates who have similar degrees and credentials.

For a candidate, entering hackathons helps potential employees connect with companies and learn a lot about how an organization works.

In the past, I would check that applicants had a math or STEM background only. Now, with the proliferation of college programs and online courses, I check if they have any credentials specific to machine learning or data science.

Rosaria Silipo, head of data science evangelism, KNIME

For an organization looking to hire a lot of people quickly, hackathons can provide a bounty of leads.

Hackathons let you create this warm pool of talent, because a lot of times when you actually go out to hire in the market, you may not be able to source the right kind of candidates with the right skill sets at a short notice, said HackerEarths Shukla.

For entry-level candidates, one of the most direct ways to learn how a company operates is through interning and a company can see if theyre a good fit.

We try to bring on interns who we can get to know before they graduate and they get to experience our culture beforehand, said David Karandish, founder and CEO at enterprise AI software-as-a-service company Capacity.

We really lead with Hey, heres the type of work youre going to do here. And we like people who are excited about the work that they do.

In the DevOps era, teams need to be increasingly cross-functional as businesses and data-driven product development come together. Good communication and collaboration skills are considered as important as a degree or a certification.

AI professionals need to explain complex topics often across multiple time zones, in a remote work setting, and be understood by a wide variety of people with various levels of technical knowledge.

No one person is ever going to know how every single thing works, noted Karandish. So organizations need people who can collaborate and coordinate together, and know when to ask for help or to bring up an important issue.

Its knowing when to ask, are we going down the right path or not, or is there a different approach?

And attitude goes hand-in-hand with collaboration.

Nobody wants to be working with a jerk, he said. They tend to not be collaborative and tend to take credit when credit isnt due. So wed like people with a high-talent-to-low-ego ratio in general.

A wide variety of companies are hiring, and an AI professional needs to understand the specific issues theyre trying to solve for their employer.

They need to have the proper domain knowledge to be able to provide precise recommendations and critically evaluate different work models, said Kamyar Shah, CEO at World Consulting Group.

To design self-running software for businesses and customers, they need to understand both the company and the issues their designs solve for that company, he said.

Problem-solving is another highly valued skill not just understanding what a problem is, but being able to come up with new solutions.

A big aspect of ML and AI is creating playbooks that have not been built before, said Wilson Pang, CTO of data company Appen. A developer needs to have the ability to try new techniques, test and learn, and continually grow through keeping up with industry trends.

Featured image by Alex Knight via Unsplash.

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Everyone's Seeking AI Engineers Here's What They Want - thenewstack.io

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Widening gaps in cybersecurity tech stacks are leaving enterprises vulnerable to debilitating attacks. Making matters worse, there are often conflicting endpoints, patch management and patch intelligence systems that partially support a small subset of all devices. CISOs tell VentureBeat that gaps in their cybersecurity tech stacks are getting wider because their legacy systems cant integrate across unified endpoint management (UEM), asset management, IT Service Management (ITSM) and cost management data available in real time to optimize cybersecurity deterrence strategies and spending.

Ivantis quickness in using AI and machine learning to take on these challenges is noteworthy. In the span of fewer than eighteen months, theyve delivered their AI-based Ivanti Neurons platform to enterprise customers and continued to innovate it. The company first introduced the Ivanti Neurons platform in July 2020, empowering organizations to autonomously self-heal and self-secure devices and self-service end users.

Since then, Ivanti has released updates and added innovations to the platform on a quarterly basis to further help customers quickly and securely embrace the future of work. For example, Ivanti recently released Ivanti Neurons for Zero Trust Access, the first AI-based solution to support organizations fine-tuning their zero trust frameworks. The company also introduced Ivanti Neurons for Patch Management, a cloud-native solution that enables IT teams to efficiently prioritize and remediate the vulnerabilities that pose the most danger to their organizations.

In the same period, Ivanti acquired MobileIron, Pulse Secure, Cherwell, RiskSense, and the Industrial Internet of Things (IIoT) platform owned by theWIIOGroup. Their total addressable market has doubled due to these acquisitions, reaching $30 billion this year, growing to $60 billion by 2025. Ivanti has 45,000 customers, providing cybersecurity systems and platforms for 96 of the Fortune 100.

Ivanti is successfully scaling its AI-based Neurons platform across multiple gaps in enterprises cybersecurity tech stacks. VentureBeat recently spoke with Ivantis CEO, Jeff Abbott, and president and chief product officer Nayaki Nayyar to gain further insight on Ivantis growth and success . The companys executives detailed how Ivantis approach to integrating AI and machine learning into its Neurons platform will help its customers anticipate, deter and learn from a wide variety of cyberattacks.

VentureBeat: Why do new customers choose an AI-based solution like Ivanti Neurons over the competing, substitute solutions in the market?

Jeff Abbott: Were looking to AI, machine learning, and related technologies to create a richer experience for our customers while continually delivering innovative and valuable new capabilities. Were leveraging AI & machine learning bot technology to solve common challenges that our customers are facing. The example I like is discovery. The process of understanding whats on a network. I talk to customers all the time, and one that comes to mind is a superintendent of a school district who said, Every six months we send out teams to go to all the various locations of various schools and see whats on the network physically or we run protocols on site. Now with your bot technology, we can do that on a nightly basis and discover whats there. Thats an example of how our unified platform increases visibility for our customers, while continually staying on top of security standards.

Its fascinating to consider all the opportunities the metadata from UEM, IT service management (ITSM) / IT asset management (ITAM), and cost management systems provide. Having the metadata from all three systems on a single pane of glass becomes very interesting to what we can tell customers about their operations down to the device level. Creating a data lake based on the metadata becomes a powerful tool. Having a broad base of contextual data to analyze with the Ivanti Neurons platform enables us to gain a new understanding of whats happening. Were relying on AI and machine learning in the context of the Ivanti Neurons platform to scale from providing basic information up to contextually intelligent insights our customers can use to grow their businesses.

Nayaki Nayyar: I was in the oil and gas industry for 15 years, working with Shell and Valero Energy for many years. So, Ive lived in the customers shoes and can empathize with three big problems theyre facing today, regardless of the industry they are in

The first is the explosive growth of edge devices, including mobile devices, laptops, desktops, wearables and, to some extent, IoT devices. Thats a big challenge that everyone has to address. Then the second problem is ransomware. Not a single day goes by without a ransomware attack. And the third is how to provide a great customer experience that equals the quality of everyday consumer experiences. Solving how to bring a consumer-grade experience into an enterprise context is an area were prioritizing today.

Our goal is to automate tasks beneath the user experience layer of our applications, so our customers dont have to worry about them; let AI, machine learning, and deep learning capabilities heal endpoints, using intelligent bots for endpoint discovery, self-healing, asset management and more. Our goal is to provide customers with an experience where the routine tasks are managed autonomously, so they dont have to. The Ivanti Neurons platform is designed to take on these challenges and more.

VentureBeat: How are you fine-tuning your algorithms to fight ransomware so that your customers dont have to become data scientists or consider recruiting a data scientist?

Nayaki Nayyar: I will highlight two distinct AI capabilities that we have to address your exact question on preventing ransomware. We have what we call Ivanti Neurons for Edge Intelligence, which provides a 360-degree view of all the devices across a network, and using NLP, weve designed the platform so its flexible enough to respond to questions and queries. An example would be, How many devices on my network are not patched correctly or have not been patched for these specific vulnerabilities? The Ivanti Neurons platform will automatically respond to simple text-based and keyword searches. So, our customers can ask a question using natural language, and the system will respond to it.

Weve also developed deep expertise in text ranking. We mine data from various social channels, including Twitter, Reddit, and publicly available sources. We then do sentiment analysis on various Common Vulnerabilities and Exposures (CVEs) that are trending and sentiment analysis on the patches. Then we provide those insights in Ivanti Neurons for Patch Intelligence. Using NLP, sentiment analysis, and AI, Ivanti Neurons for Patch Intelligence provides our customers administrators with the insights they need to prioritize which CVEs have the highest risks for their organization and then remediate those issues immediately. That doesnt require data scientists to be employed by our customers. All of that is being embedded into our stack, and we make it simple for customers to consume it.

Jeff Abbott: Were also constantly doing research on ransomware and vulnerabilities. In fact, we just released our Ransomware Spotlight Year-End Report. The analysis shows that the bad actors target organizations that are not keeping up with CVEs.

Not keeping up with zero-day vulnerabilities and defining a plan for addressing them can make any organization a gazelle in the middle of the field. So, as Nayaki said, were providing patch intelligence to help our customers prioritize which vulnerabilities are most important to address first. One of the factors that led to us acquiring RiskSense is their extensive data set on detection. Were using the data to provide forward intelligence on the open vulnerabilities and help our customers anticipate and fix them quickly. Were seeing that our mid-tier and SMB accounts need patch intelligence as much as our enterprise customers.

VentureBeat: How does AI deliver measurable value for customers? How do you quantify that and know you are meeting expectations with customers, that youre delivering value?

Nayaki Nayyar: For many years, solving security, IT or asset issues was a reactive process. Every customer called or filed a ticket right after the issue happened, reporting the issue. The ticket was created, then it was routed to the right service desk agent to solve it. But that took too much time, possibly ten days later or even a month later, before the ticket was resolved.

The Ivanti Neurons platform is designed to detect security, IT, asset, endpoint, or discovery issues before the end-user knows that issue will happen. Our bots are also designed to be self-healing and they can detect whether its a configuration drift that has happened on a device, or whether it is a security anomaly or a performance issue. Bots automatically heal those issues, so end users dont even have to create a ticket and route the ticket to get a resolution.

If we can help customers reduce the number of issues by 30% or more before end users even create tickets, then that represents a massive cost saving. Not to mention the speed and accuracy at which those services are provided.

VentureBeat: Which customer needs are the most urgent and best met by expanding the AI capabilities of your Ivanti Neurons platform?

Nayaki Nayyar: Today, discovering unknown assets or endpoints is an urgent, high-priority requirement. The greatest challenge is blind-spot detection within an organization. Weve architected Ivanti Neurons to detect blind spots across enterprise networks. Our customers are using Neurons to identify assets regardless of their locations, whether they are in data centers, cloud assets, endpoints, or IoT assets.

Discovery is most often step one for our customers on the Ivanti Neurons platform because it helps them turn their unknown assets into known assets immediately. They dont need to remediate and self-heal devices right away; that can come later in the asset cycle. Ivanti Neurons for Discovery are a critically important solution that customers get immediate benefit from and then can expand upon.

Most customers have what we call a Frankensteins mess of tools and technologies to manage their devices By combining our Neurons platform with the technologies from our recently acquired companies, were now providing a single pane of glass, so an analyst can log in, see what device types are on the network, and manage any endpoint security or asset management problems right from there.

Jeff Abbott: Patching is overly complex and time-consuming, and thats a huge problem our customers also face. Ivanti Neurons for Patch Management and Patch Intelligence help solve those challenges for our customers. Were focused on improving user experiences to make AI and NLP-based patch management and intelligence less intimidating. Our focus is specifically on helping our customers keep up with the latest zero-day vulnerabilities and CVEs that could impact them. We focus on solving the biggest risk areas first using Ivanti Neurons, alleviating the time-consuming work our customers would otherwise have to go through.

VentureBeat: What are the Ivanti Neurons platforms top three design goals, and how do you benchmark success for those?

Jeff Abbott: Our primary goals are for the Ivanti Neurons platform to discover devices, and then self-heal and self-secure themselves using AI-based workflows and technologies. Our internal research shows that customers using Neurons are experiencing over 50% reductions in support call times. Theyre also eliminating duplicate work between IT operations and security teams and reducing the number of vulnerable devices by 50%. These stats are all from customer surveys and anonymized actual results. Ivanti Neurons is also contributing to reducing unplanned outages by 63%.

Nayaki Nayyar: Adding to what Jeff said, the entire architecture is container-based. We leverage containers that are cloud-agnostic, meaning we can deploy them anywhere. So, one goal is not just to deploy to the cloud, but also to drop these containers on the edge in the future so that we can process those workloads at the edge, closer to where the data is getting generated.

The platform is also all API-based, so the integration we do within the stack is all based on APIs, This means that our customers dont need to have the entire stack. They can start anywhere and evolve at their own pace. They can start in the security space in patch management and move from there. Or they can start in service management or discovery. They can start anywhere and evolve everywhere. And we also recognize that they dont need to have just Ivantis entire stack. They can be using two or three pillars from us and other systems and platforms from other vendors.

VentureBeat: Do you see customers moving to an AI-based platform to scale zero trust initiatives further out?

Nayaki Nayyar: Yes, we have a large manufacturing customer who was evolving from VPN-based access into zero trust. This is a big paradigm shift. With VPN-based access, youre pretty much giving users access to everything, whereas, with a zero-trust approach, youre continuously validating and authenticating every application access. As the customer was switching to zero trust, their employees were running into many access denied issues. The volume of tickets coming into the service deck spiked by 500%.

The manufacturing customer started using Ivanti Neurons with AI and ML-based bots to detect what kind of access issues users were having and self-heal those issues based on the right amount of access. The ticket volume immediately went down. So, it was a great example of customers evolving beyond VPN to zero trust access; our technology can help customers advance zero-trust and solve access challenges.

VentureBeat: What additional verticals are you looking at beyond healthcare? For example, will there be an Ivanti Neurons for Supply Chain Management, given how many constraints they have become in the last year to eighteen months, for example?

Nayaki Nayyar: Im extremely passionate about IoT and whats happening with edge devices today. The transformation that we see at the edge is phenomenal. Were designing support for edge devices into the Ivanti Neurons platform today, giving our customers the flexibility of managing IoT assets.

Healthcare is one of the verticals where we have gone deep into discovering and managing our customers many healthcare devices, especially those you see in a hospital setting like Kaiser.

Manufacturing facilities or shop floor is another area we are exploring. Our customers have different types of ruggedized IoT devices that we can apply the same principles of discovering, managing, and providing security to the IoT assets on the shop floor. In the future, we also plan on extending into the telco space. We have large telcos as customers, and theyve been asking us to go more and more into the telco IoT world.

Our telco customers also tell us they would like to see greater support for ruggedized devices their field technicians use out in the field. Retailers are also expressing an interest in supporting ruggedized devices, which is an area were exploring today.

Jeff Abbott: The public sector comprising federal, state, and local have unique requirements, of which Nayaki and I have had several conversations about. Many capabilities for vertical markets are still very horizontal. Were seeing that as organizations discover the nuances of their use of edge computing and edge technology, more specialized vertical market requirements will become more dominant. I think were covering 90% or more of the security requirements now. Thats especially the case in discovery, patch management, and patch intelligence.

VentureBeat: How do you integrate an AI-based platform into a legacy system tech stack or infrastructure? What are the most valuable technologies for accomplishing that, for example, APIs?

Nayaki Nayyar: We have a pretty strong connector base with existing systems. I wont call them a legacy. We need to coexist with existing systems, as many have been installed for 10 to 15 years at a minimum in many organizations. To accomplish this, we have 300 or more connectors out of the box that can be leveraged by our customers, resellers, and partners. Were committed to continually strengthening our ecosystem of partners to provide customers with the options they need for their unique integration requirements.

VentureBeat: Could you share the top three lessons Ivanti has learned, designing intuitive user experiences to guide users using AI-based applications?

Jeff Abbott: I think the most important lesson learned is to provide every customer, from SMBs to enterprises, data-driven insights that validate AI is performing appropriately. Ensuring that self-healing, self-servicing, and all supporting aspects of Ivanti Neurons protect customers assets while also contributing to more efficient ITSM performances.

When it comes to preventing ransomware attacks, the key is to always provide users with the option of performing an intuitive double-check. One day your organization could be very healthy. But, on the other hand, you may not be paying attention to the intuitive signals from AI, which could lead to the organization falling victim to an attack. Taking an active position on security, which includes knowing your organizations tools and understanding what they can achieve, is important.

Nayaki Nayyar: User experiences require a three-prong approach. Start by concentrating first with humans in the loop, recognizing the unique need for contextual intelligence. Next, add the need for augmented AI, and then the last level of maturity is humans out of the loop.

For customers, this translates into taking the three layers of maturity and identifying how and where user experience designs deliver more contextual intelligence. The goal with Ivanti Neurons is to remove as many extraneous interactions with users as possible, saving their time only for the most unique, complex decision trade-offs that need to be made. Our goal is to streamline routine processes, anticipate potential endpoint security, patch management, and ITSM-related tasks, and handle them before a user sees their impact on productivity and getting work done.

VentureBeat: With machine learning models so dependent on repetitive learning, how did you design the Ivanti Neurons platform and related AI applications to continually learn from data without requiring customers to have data scientists on staff?

Nayaki Nayyar: Were focused on making Ivanti Neurons as accessible as possible to every user. Weve created an Employee Experience Score, a methodology to identify how effective our customers experiences are on our platform to achieve that. Using that data, we can tell which application workflows need the most work to further improve usability and user experiences and which ones are doing so well that we can use them as models for future development.

Were finding this approach to be very effective in quantifying [whether] were meeting expectations or not by individual, employee, division, department, and persona. This approach immediately gets organizations out of using ticket counts as a proxy for user experience. Closing tickets alone is not the SLA that needs to be measured alone. Its more important to quantify the entire experience and seek new ways to improve it.

VentureBeat: How do you evaluate potential acquisitions, given how your product and services strategy moves in an AI-centric direction? What matters most in potential acquisitions?

Jeff Abbott: Were prioritizing smaller acquisitions that deliver high levels of differentiation via their unique technologies first, followed by their potential contributions to our total addressable markets. Were considering potential acquisitions that could strengthen our vertical tech stack in key markets. Were also getting good feedback directly from customers and our partners on where we should look for new acquisitions. But Id like to be clear that its not just acquisitions.

We also have very interesting partnerships forming across industries, focusing on telco carriers globally. Some of the large hardware providers have also proposed interesting joint go-to-market strategies, which we think will be groundbreaking with the platform. Were also looking at partnerships that create network effects across our partnership and customer base. Thats what were after in our partnership strategy, especially regarding the interest were seeing on the part of large telco providers today. So, were going to be selective. We will go after those that put us in a differentiation category. The good news is that many nice innovative companies are getting into that level of maturity.

Where we can partner or acquire them, were focused on not disrupting the trajectory theyre on. It creates a much bigger investment portfolio to continue to advance those solutions.

Nayaki Nayyar: Were very deliberate in what acquisitions we do for two primary reasons. One is to strengthen the markets that we play in. We compete in three markets today, and our recent acquisitions strengthen our position in each. Our goal is to be among the top two or top three in each market were competing in. An integral part of our acquisition strategy is looking at how a potential acquisition can increase our entire addressable market and gain access to adjacent markets that we can start to grow in.

We are in three markets: UEM, security, and service management. As were converging these three pillars into our Ivanti Neurons platform, we are evolving into adjacent markets like DEX (Digital Experience Management) So far, our approach of relying on acquisitions to strengthen our core three markets is working well for us. To Jeffs point, strengthening what we have to further to be a top vendor in these markets is working, delivering strong, differentiated value to our customers.

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How Ivanti hopes to redefine cybersecurity with AI - VentureBeat

The product designing sector is creating new opportunities as it receives an AI transformation

Over the last couple of years, AI has been making great strides across distinct global industries. AI technologies are integrated with businesses for promoting efficiency and developing automation to carry out rigorous tasks and other activities that relate to stocks, finance, marketing, healthcare, and that too, using smart devices. Tech experts believe that between the years 2040 to 2050, AI will be capable of performing intellectual tasks that were traditionally only carried out by humans. Currently, artificial intelligence is creating a myriad of opportunities for businesses and global industries to achieve the best standards of quality while delivering consumer products or services. Similarly, AI has also transformed product designing and development to quite an extent. AI can help solve issues in the field of creativity and solve complex problems. When it comes to product designing and development, the role of AI cannot be ignored. Integrating AI in product designing and development has entirely transformed the dynamics of the relationship between businesses and consumers.

From startups to large enterprises, everyone is racing to get their new products launched, and AI and machine learning are making robust contributions to this process. Rapid advances in AI-based applications, products, services, and platforms have also driven the consolidation of the IoT market. The IoT platforms provide concentrated solutions for business challenges in vertical markets that stand the best chance of surviving the upcoming IoT advancements. Since AI and ML are getting ingrained in product designing, more related platforms and products now need to adapt to the upcoming circumstances. An advanced and efficient product design company will integrate robust IoT products and AI services to ensure that the customers enjoy the best quality products and the enterprise teams seamlessly retain more customers.

Without sounding too futuristic or advanced, it is quite safe to say that AI will most likely outrun human resources in terms of intellectual activity processing in the near future. One of the many critical aspects of the phenomenon of AI interfering in human lives and experiences is that it has enhanced customer expectations for high-quality products. As a result, the global market is drenched in competitiveness as enterprises strive towards better products with superior design and enhanced performance standards. And slowly, it has turned into a mandate instead of being just a necessity.

Major tech companies are using AI to reduce energy and financial costs while designing and developing new products. AI can also be helpful to human creative leads in product designing by taking the mundane and rigorous tasks off their hands. The technology is meant to help professionals to have an easier, more fulfilling life but not take it away. Furthermore, one of the toughest phases of new product development is designing its user experience. To ensure the success of a product, it is crucial for enterprise leaders to ensure that the product is relatable enough for the users. The design process requires massive amounts of creative muscle, especially when the team must intricately think about how the product will be used, validate those ideas, and create something out of the box. Using an AI-driven brainstorming tool for such purposes might serve as a useful tool.

Besides this, AI is efficiently capable of letting the team know beforehand during the design phase whether or not a specific design will be successful or is deemed to fail. The system can explore the proposed user flow and determine whether a user can complete the desired action or not. This saves the company from having to build multiple iterations of a product for testing.

AI is here to stay. It does not matter if the industry is tech-based or otherwise, AI-driven systems and algorithms will create groundbreaking innovations and lead global enterprises towards immense profitability to help businesses reach new highs.

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Analytics Insight is an influential platform dedicated to insights, trends, and opinions from the world of data-driven technologies. It monitors developments, recognition, and achievements made by Artificial Intelligence, Big Data and Analytics companies across the globe.

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Disrupting Product Designing with the Much-Needed AI Makeover - Analytics Insight

Artificial intelligence (AI) promises to be one of the most transformative technologies of our time. It has already proven it can reliably complete complex tasks almost instantaneously, eliminating the need for days or even weeks of human input in many cases.

The challenge for companies developing this advanced technology is building a business model that can deliver it efficiently since AI is a brand-new industry with little existing precedent. That's what makes C3.ai ( AI -1.96% ) a trailblazer, as it's the first platform AI provider helping companies in almost any industry access the technology's benefits.

C3.ai just reported its fiscal 2022 third-quarter earnings result, and it revealed continued growth across key metrics, further cementing the case for owning its stock for the long run.

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As more of the economy transitions into the digital realm, a growing number of companies will find themselves with access to game-changing tech like artificial intelligence. In the second quarter of fiscal 2022, C3.ai said it was serving 14 different industries, double the amount from the corresponding quarter in the previous year. It indicates that more sectors are already proactively seeking the benefits of AI.

One of those sectors is oil and gas, which represents the largest portion of C3.ai's total revenue. The company has a long-standing partnership with oil giant Baker Hughes. Together, the two companies have developed a suite of AI applications to predict critical equipment failures and reduce carbon emissions in drilling and production operations.

Shellis a core customer of these applications, and it's using them to monitor 10,000 devices and 23 large-scale oil assets, with the technology processing 1.3 trillion predictions per month.

In the recent Q3 of fiscal 2022, C3.ai revealed a new partnership with the U.S. Department of Defense worth $500 million over the next five years. It's designed to accelerate the adoption of AI applications across the defense segment of the federal government.

But some of C3.ai's most impressive partnerships are those with tech behemoths like Microsoft and Alphabet's Google. They're collaborating with C3.ai to deploy AI applications in the cloud to better serve their customers in manufacturing, healthcare, and financial services, among other industries.

From the moment a potential customer engages C3.ai, it can take up to six months to deploy their AI application. Therefore, it's important to watch the company's customer count as it can be a leading indicator for revenue growth in the future.

In fiscal Q3 2022, C3.ai reported having 218 customers, which was an 81% jump over Q3 2021. Over the same period, remaining performance obligations (which are expected to convert to revenue in the future) climbed by 90% to $469 million.

Since quarterly revenue grew a more modest 42% in the same time span, both of the above metrics hint at a potential revenue-growth acceleration over the next few years. The company has also raised its sales guidance twice so far in the first nine months of fiscal 2022, albeit by just 2% in total, now estimating $252 million in full-year revenue.

C3.ai has been a publicly traded company for a little over a year, listing in December 2020. It quickly rallied to its all-time high stock price of $161 before enduring a painful 87% decline to the $20 it trades at today. The company hasn't grown as quickly as investors anticipated, and it also hasn't achieved profitability yet.

But right now, C3.ai trades at a market valuation of $2.1 billion, and it has over $1 billion in cash and short-term investments on its balance sheet. Put simply, investors are only attributing a value of around $1 billion to its AI business despite over $250 million in revenue expected by the close of fiscal 2022 and a portfolio of A-list customers.

Moreover, C3.ai has a gross profit margin of 80%, affording it plenty of optionality when it comes to managing expenses. This places it in a great position to eventually deliver positive earnings per share to investors once it achieves a sufficient level of scale.

While C3.ai stock carries some risk, especially in the middle of the current tech sell-off, by many accounts it's beginning to look like an attractive long-term bet. Advanced technologies like AI will only grow in demand over time, and this company is a great way to play that trend.

This article represents the opinion of the writer, who may disagree with the official recommendation position of a Motley Fool premium advisory service. Were motley! Questioning an investing thesis even one of our own helps us all think critically about investing and make decisions that help us become smarter, happier, and richer.

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1 Artificial Intelligence Growth Stock to Buy Now and Hold for the Long Term - The Motley Fool