Monthly Archives: May 2021

For the past year and a half, life has been both massively challenging and exhilarating for business technology vendors and their customers and partners. As the COVID-19 pandemic forced companies to fundamentally rethink the way workers, managers and executives performed essential tasks, vendors responded with innovative new solutions and services.

Organizations adopted and deployed those offerings at unheard-of speeds, accomplishing in weeks or months what once would have taken years. The result led to unusual or unique accomplishments. As IBM CEO Arvind Krishna pointed out during his IBM Think 2021 keynote address last week: I venture to say that 2020 was the first time in history that digital transformation spending accelerated despite GDP declining.

As vaccinations bring the pandemic under control and things return slowly to normal, how will businesses preserve or extend the transformational solutions they adopted? At IBM Think, Krishna and his leadership team offered valuable insights and new solutions to consider.

The announcements at Think 2021 mostly centered on areas that have long been focal points for IBM (and some of its competitors): hybrid cloud, artificial intelligence and quantum computing. What was different this time around was the practical and business value offered by new solutions and features.

Take AI, for one. Many if not most AI projects and efforts center on or have been designed to support large-scale moonshot efforts that underscore their owners far-sighted vision and willingness to take on big challenges. That can be both dramatic and problematic, given how often these projects complexities lead to setbacks, delays and failure. There is also a tendency toward forest for the trees confusion manifested by mistaking the results of complementary efforts, such as machine learning for AI itself.

During its decades-long involvement in AI R&D, IBM has been involved in its own share of moonshot projects. However, the AI solutions announced at Think were more in the line of practical innovations designed to maximize dependable business benefits. For example, AI enhancements drive the new AutoSQL function in IBMs Cloud Pak for Data that enables customers to receive queries to data in hybrid multi-cloud environments (on-premises, private clouds or any public cloud) up to 8X faster and at half the cost of prior solutions. The new intelligent data fabric in Cloud Pak for Data will automate complex management functions by using AI to discover, understand, access and protect information in distributed environments.

Another new AI-powered IBM solution is Watson Orchestrate, which is designed to increase the personal productivity of employees in sales, human resources, operations and other business functions by automating and simplifying business processes. The AI engine in Watson Orchestrate automatically selects and sequences pre-packaged skills required to perform tasks and connects them with associated applications, tools, data and historical details. There are no IT skills required for users. Instead, they can use natural language collaboration tools, such as Slack and email, to initiate work. Watson Orchestrate also connects to popular applications, including Salesforce, SAP and Workday.

Similarly, the new Maximo Mobile solution uses Watson AI to enhance the performance and productivity of field technicians who work on bridges, roads, production lines, power plants, refineries and other physical industrial and infrastructure assets. Users can use Maximo Mobile virtually anywhere, even in remote locations, to access operational data, human assistance and digital twins (virtual representations that act as real-time digital counterparts of physical objects or processes) to complete vital tasks.

The practical melding on AI and automation to better manage or perform complex processes was one of the most profound themes at IBM Think. In his keynote, CEO Krishna noted that automation is nothing new, Its been around for centuries. Industrial automation gave manufacturing companies economies of scale and cost advantage in making things such as cars and household appliances. The most profound economies of scale are no longer only about manufacturing; theyre about producing breakthrough ideas by people leveraging technology automation to tap into their knowledge.

Krishna addressed a common concern: That technologically-enabled automation will damage or eliminate traditional jobs. The future is not about how AI is going to replace jobs but how it will change jobs by bringing in what I call AI complementarity. What I mean by that is that AI is very good at accomplishing things that we dont particularly like doing, and vice versa.

Krishna also noted that AI-enabled automation can have a remarkable impact on workers and businesses alike. Research shows that high-powered automation can help you reclaim up to 50% of your time to focus on what matters most. IDC predicts that by 2025, AI-powered enterprises will see a major increase in customer satisfaction. Let me put a number on it: up to 1.5 x higher net promoter scores compared to the competition. Human ingenuity leveraging technology is what is going to drive a competitive advantage today.

This is a profound message for IBMs customers and partners, many of whom have been significantly, negatively impacted by Covid-19. As the pandemic eases and businesses work to regain forward momentum, significantly improving both process efficiency and customer satisfaction would be hugely beneficial.

Of course, AI-infused automation wasnt the only subject highlighted at IBM Think. The company also announced other new solutions focused on making life easier for enterprise IT professionals, including Project CodeNet, a large-scale, open-source dataset comprised of 14 million code samples, 500 million lines of code and 55 programming languages. Project CodeNet is designed to enable the understanding and translation of code by AIs and includes tools for source-to-source translation and transitioning legacy codebases to modern code languages. Another new AI-enabled solution, Mono2Micro, is a capability in WebSphere Hybrid Edition that is designed to help enterprises optimize and modernize applications for hybrid clouds.

Not surprisingly, IBM announced significant advancements in its Quantum computing efforts. Qiskit Runtime is a new software solution containerized and hosted in the hybrid cloud. In concert with improvements in both the software and processor performance of IBM Q quantum systems, Qiskit Runtime can boost the speed of quantum circuitsthe building blocks of quantum algorithmsby 120X, vastly reducing the time required for running complex calculations, sich as chemical modeling and financial risk analysis.

Think 2021 featured testimonials by numerous enterprise customers, including Johnson & Johnson, Mission Healthcare, NatWest Bank and CVS Health that underscored the benefits they are achieving with IBM solutions, including hybrid cloud, Watson AI and IT modernization. IBM also unveiled new competencies and skills training in areas including hybrid cloud infrastructure, automation and security. These were developed as part of the $1 billion investment the company has committed to supporting its partner ecosystem.

So, what were the final takeaways from IBM Think 2021? First and foremost, the company and its leadership are focused on helping enterprise customers and partners survive the challenges of the Covid-19 pandemic and prepare them to thrive as business and daily life resumes.

In some cases, companies will hope to return to and regain their past trajectories and IBMs portfolio of solutions should serve them well. But in many other instances, businesses will be pushing toward a new normal by adopting new and emerging innovations, including AI, advanced automation and hybrid cloud computing. Those organizations should have come away from Think 2021 knowing that IBM has their back, whether it is by providing the offerings they need immediately or investing in new solutions and services that will support future growth.

A final point about IBMs efforts in AI: The messaging at Think 2021 does not mean that the company is abandoning large-scale projects or long-term goals. But rather than focusing mostly or entirely on moonshot projects, the new IBM solutions infused with AI complementarity show that the company has its feet firmly on the ground. That business-focused message should and will sit well with IBMs enterprise customers and partners.

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IBM Think 2021: AI, Automation, Hybrid Cloud and Practical Innovation | eWEEK - eWeek

In a recent survey, 2021 Thriving in an AI World, KPMG found that across every industrymanufacturing to technology to retailthe adoption of artificial intelligence (AI) is increasing year over year. Part of the reason is digital transformation is moving faster, which helps companies start to move exponentially faster. But, as Cliff Justice, US leader for enterprise innovation at KPMG posits, Covid-19 has accelerated the pace of digital in many ways, across many types of technologies. Justice continues, This is where we are starting to experience such a rapid pace of exponential change that its very difficult for most people to understand the progress. But understand it they must because artificial intelligence is evolving at a very rapid pace.

Justice challenges us to think about AI in a different way, more like a relationship with technology, as opposed to a tool that we program, because he says, AI is something that evolves and learns and develops the more it gets exposed to humans. If your business is a laggard in AI adoption, Justice has some cautious encouragement, [the] AI-centric world is going to accelerate everything digital has to offer.

Business Lab is hosted by Laurel Ruma, editorial director of Insights, the custom publishing division of MIT Technology Review. The show is a production of MIT Technology Review, with production help from Collective Next.

This podcast episode was produced in association with KPMG.

2021 Thriving in an AI World, KPMG

Laurel Ruma: From MIT Technology Review, Im Laurel Ruma, and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.

Our topic today is the rate of artificial intelligence adoption. Its increasing, and fast. A new study from KPMG shows that its accelerating in specific industries like industrial manufacturing, financial services, and tech. But what happens when you hit the gas pedal but havent secured everything else? Are you uneasy about the rate of AI adoption in your enterprise?

Two words for you: covid-19 whiplash.

My guest is Cliff Justice, who is the US leader for enterprise innovation for KPMG. He and his group focus on identifying, developing, and deploying the next generation of technologies, services, and solutions for KPMG and its clients. Cliff is a former entrepreneur and is a recognized authority in global sourcing, emerging technology such as AI, intelligent automation, and enterprise transformation. This episode of Business Lab is produced in association with KPMG. Cliff, thank you for joining me on Business Lab.

Cliff Justice: Its great to be here. Thanks for having me.

Laurel: So, were about to take a look at KPMGs survey results for its 2021 Thriving in an AI World report, which looks across seven industries. Why did KPMG repeat that survey for this year? What did you aim to achieve with this research?

Cliff: Well, artificial intelligence is evolving at a very rapid pace. When we first started covering and investing in artificial intelligence probably seven years ago, it was at a very nascent form. There were not very many use cases. Many of the use cases were based on natural language processing. About 10 years ago was when the first public use case of artificial intelligence made the headlines with IBM Watson winning Jeopardy. Since then, youve seen a very, very rapid progression. And this whole field is evolving at an exponential pace. So where we are today is very different than where we were a year or two ago.

Laurel: It does seem like just yesterday that IBM was announcing Watson, and the exponential growth of artificial intelligence is everywhere, in our cars, on our phones. Were definitely seeing it in more places than just this one kind of research case of it. One of the headlines from the research is that theres a perception that AI might be moving too fast for the comfort of some decision-makers in their respective industries. What does too fast look like? Is this due to covid-19 whiplash?

Cliff: Its not due to covid whiplash necessarily. The covid environment has accelerated the pace of digital in many ways, across many types of technologies. This is where we are starting to experience such a rapid pace of exponential change that its very difficult for most people to understand the progress. For any of us, even myself who works in this field, its very difficult to understand the progress and the pace of change. And getting an enterprise readygetting the people, the process, the enterprise systems, the risk, the cyber protections prepared for a world that is powered more and more by artificial intelligenceits difficult in normal circumstances. But when you do combine the digital acceleration and adoption thats taking place as a result of covid, along with the exponential development and evolution of artificial intelligence, its hard to understand the opportunities and threats that are posed to an organization.

Even if one could fully wrap their head around the progress of artificial intelligence and the potential of artificial intelligence, changing an organization and changing the mindset and the culture in a way to adopt and benefit from the opportunities that artificial intelligence poses and also protect against the threats take some time. So, it creates a level of anxiety and caution which is, in my view, well justified.

Laurel: So, speaking of that caution or planning needed to deploy AI, in a previous discussion at MIT Technologies Reviews EmTech Conference in 2019, you said that companies needed to rethink their ecosystem when deploying AI, meaning partners, vendors, and the rest of their company, to get everybody to come up to speed. At the time, you mentioned that would be the real challenge. Is that still true? Or do you think now that everything is progressing so quickly, thats the discomfort that some executives may be feeling?

Cliff: Well, thats true. It is still true. The ecosystem that got you to a level in more of an analog-centric world is going to be very different in a more AI-centric world. That AI-centric world is going to accelerate everything digital has to offer. What I mean by digital are the new ways of workingthe digital business models, the new ways of developing and evolving commerce, the ways we interact and exchange ideas with customers and with colleagues and coworkers. All of these are becoming much more digital-centric, and then artificial intelligence becomes one of the mechanisms that evolves and progresses the way we work and the way we interact. And it becomes a little more like a relationship with technology, as opposed to a tool that we program because AI is something that evolves and learns and develops the more it gets exposed to humans.

Now that we have much more human life-perceptive capabilities, thanks to the evolution of deep learning, (so by that, today, I mean more computer vision), technology is able to take on much more of the world than we were before. So understanding what technology, what AI, all of the capabilities that AI can bring and enhance and augment human capabilities is critical. Reestablishing and redeveloping the ecosystem around your business and around your enterprise is important. I think the bigger and more long-term issue though is culture, and its the culture of the enterprise that youre responsible for, that ones responsible for. But its also harnessing the culture, the external culture, the adoption, and the way you work with your customers, your vendors, suppliers, regulators, and external stakeholders. The mindset evolution is not equal in all of those stakeholder groups. And depending on the industry that youre operating in, it could be very unequal in terms of the level of adoption, the level of understanding, the ability, and the comfort to work with technology. And as that technology becomes more human-like, and were seeing that in virtual assistants and with those types of technologies, its going to be a bigger chasm to cross.

Laurel: I really like that phrasing of thinking of AI as a relationship with technology versus a tool, because that really does state your intentions when youre entering this new world, this new relationship, and that youre accepting that constant change. Speaking of the survey and various industries, some of the industries saw a significant increase in AI deployment like financial, retail, and tech. But here was it that digital transformation need or covid, or perhaps other factors that really drove that increase?

Cliff: Well, covid has had an acceleration impact across the board. Things that were in motionwhether these were adoption of digital technologies or growth or a change in consumer behaviorall of those trends that were in place before covid accelerated them. And that includes business models that were on the decline. We saw the trends that were happening in the malls. Thats just accelerated. Weve seen the adoption of technology thats accelerated. There are industries that covid has less of an effect on, not a zero effect, but less of an effect. Banking, financial services are less affected by covid than retail, hospitality, travel, logistics. Covid has really accelerated the change thats occurring in those industries.

AI, separate from covid, has a material impact across all of these. And as our survey said, industrial manufacturing, the use of robotics, the use of computer vision, artificial intelligence to speed productivity, and improved efficiency have really begun to become mainstream and at scale in industrial manufacturing. Same thing with financial services, consumer interaction has been improved with artificial intelligence in those areas. Technology, not surprisingly, has fully adopted AI or pretty close to fully adopted AI. And then weve seen a dramatic increase in retail as a result of AI. So online shopping, the ability to predict consumer demand has been a strong use case for AI in those industries.

Laurel: So, the laggards though, laggard industries were healthcare and life sciences at only, I say only, a 37% increase in adoption from last years survey. Thats still a great number. But do you think thats because fighting covid was the priority or perhaps because they are regulated industries, or there was another reason?

Cliff: Regulation is a common theme across those laggards. You have government, you have life sciences, healthcare. Financial services, though, is regulated too, and theyre a large adopter, so it cant be the only thing. I think the hypothesis around covid is probably more plausible because the focus in life sciences has been getting the vaccine out. Even though from our point of view and from what we see, government is a massive adopter. Just in terms of the potential within government, its still behind. But the sheer numbers and the sheer amount of activity thats taking place in government when you compare it to private enterprise is still pretty impressive. Its just that youre dealing with such a large-scale change and a lot more red tape and bureaucracy to make that change within a government enterprise.

Laurel: For sure. You mentioned earlier the industrial manufacturing sector, and that sector saw 72% of business leaders were influenced by the pandemic to speed AI adoption. What does that actually mean for consumers in that industry, as well as that sector as a whole?

Cliff: When I look at these numbers, theres not going to be an industry that is not affected by AI. The industries that are going to adopt it sooner and more rapidly or have an impact as a result of the pandemic, that is almost all been driven by remote work, the inability to get resources to a location, the impetus to drive automation, and AI being one of the foundational elements of automation. Because if you look at other parts of the survey where we ask, Where are the biggest benefits? its going to be found in efficiency and productivity. Thats fairly consistent across all industries when you look at where AI is being applied. So automation, productivity, predictive analytics, all of these areas are being driven by these themes around productivity. The use cases are different based on the industry, but the needs are very similar. The overarching themes and the overarching needs are very similar. You had some industries that were just impacted by the pandemic differently.

Laurel: Excitingly, maybe a difference in industrial manufacturing though, as you mentioned, are robotics. So a bit of our hardware play versus always software.

Cliff: Right. Yeah, in industrial manufacturing, youre seeing a retooling of factories. Youre seeing what some people call the Tesla effect, where there is a focus on the transformation and the automation of factorieswhere building the factory is almost as important as the product itself. Theres a lot of debate and a lot of discussion in that sector around how much to automate, and is there too much automation? I think in some of these public events where youve seen a rapid ramp-up in production where automation was used, youve seen some backing off of that as well. Too much technology can actually have counterproductive consequences and impact because there has to be human involvement in decision-making and the technology just isnt there yet. So, a lot of changes happening in that space. Were seeing a lot of evolution, a lot of new types of technologies. Deep learning is allowing more computer vision, more intelligent automation to take place in the manufacturing process within the factories.

Laurel: Speaking of keeping humans involved in these choices and ideas and technologies, strong cybersecurity is a challenge, really, for everybody, right? But the bad guys are increasingly using AI against companies and enterprises, and your only response and defense is more AI. Do you see cybersecurity specifically being an area that executives across the board accelerate spending for?

Cliff: Well, youre exactly right, cybersecurity is one of the biggest threats as technology advances, whether its AI-powered by classical computing or five or 10 years down the road when we have quantum computing made available to governments or to corporations. The security risks are going to continue to accelerate. AI is certainly an offense, but its a defense as well. So, predictive analytics using AI to predict threats, to defend against threats that are posed by AI, which are increasing the sophistication of penetration, phishing, and other ways to compromise the system. These technologies are sort of in an arms race between, as you said, the good guys and the bad guys. Theres no end in sight to that as we start to move into an era of real change, which is going to be underpinned by quantum computing in the future. This will only accelerate because you will need a new type of post-quantum cryptography to defend against the threats that quantum computers could pose to a security organization.

Laurel: Its absolutely amazing how fast, right? As we were saying, exponential growth especially with quantum computing, perhaps around the corner, five, 10 years, that sounds about right. The research though, does come back and say that a lot of respondents think their companies should have some kind of AI ethics policy and code of conduct, but not many do, not many do. So those that do are smaller companies. Do you think its just a matter of time before everyone does or its a board requirement even to have these AI ethics policies?

Cliff: Well, we do know that this is being discussed at the regulatory level. There are significant questions around where the government should step in with regulatory measures and where self-policing AI ethics... How does your marketing organization target behavior in its customer base? And how do you leverage AI to use the psychological profiles to enable sales? There are some ethical decisions that would have to be made around that, for example. The use of facial recognition in consumer environments is well debated and discussed. But the use of AI and the ethical use of AI targeting the psychology of consumers, I think that debate has just started largely this summer with some documentaries that came out that showed how social media is using AI to target consumers with marketing products and how that can be misused and misapplied by the bad guys.

So, yeah, this is just the tip of the iceberg. What were seeing today is just the initial opening statements when it comes to how far should we go with AI and what are the penalties that are applied to those who go further than we should, and are those penalties regulated by the government? Are they social penalties and just exposure or are these things that we need laws and rules that have some teeth for violating these agreed-upon ethics, whatever they may be?

Laurel: Its a bit of a push-me, pull-you situation, right? Because the technology is advancing really quickly, but societal or regulations may be a bit lagging. And at the same time, companies are not necessarily, maybe in some cases, adopting AI as quickly or are having problems staffing these AI initiatives. So, how are companies trying to keep up with talent acquisition, and should enterprises start looking, or perhaps already have, been looking at upskilling or training current employees how to use AI as a new skill?

Cliff: Yeah, these are very hard problems. If you look at the study and dive in, youll see the difference between large companies and small companies. I mean, the ability to attract talent that has gone through years and years of training in advanced analytics, computer engineering, deep learning, machine learning, and understanding the complexities and the nuances of training the weights and biases of complex, multilevel, deep learning algorithmsthat talent is not easy to come by. Its very difficult to take a classical computer engineer and retrain them in that type of statistical-based artificial intelligence, where youre having to really work with training these complex neural networks in order to achieve the goals of the company.

Were seeing the tech companies offer these services on the cloud, and that is a way to access artificial intelligence and access some of these tools is through the subscription to APIs, application program interfaces, and applying those APIs to your platforms and technologies. But to really have a competitive advantage, you need to be able to manipulate and develop and control the data that goes into training these algorithms. In todays world, artificial intelligence is very, very data hungry, and it requires massive amounts of data to get accurate and high-quality output. That data accrues to the largest companies and thats reflected in their valuation. So, we see who those companies are. A lot of that value is because of the data that they have access to. And the products that theyre able to produce are based on much of that data. Those products many times are powered by artificial intelligence.

Laurel: So back to the survey, one last data point here, 60% of respondents say that AI is at least moderately to fully functional in their organization. Compared to 10 years ago, that does seem like real progress for AI. But not everyone is there yet. What are some steps that enterprises can take to become more fully functional with AI?

Cliff: This is where I go back to what I said last year, which is to re-evaluate your ecosystem. Who are your partners? Who is bringing these capabilities into your business? Understand what your options are relative to the technology providers that are giving you access to AI. Not every company is going to be able to just go hire an AI expert and have AI. These are technologies, like I said, theyre difficult to develop. Theyre difficult to maintain. Theyre evolving at a lightning-fast exponential pace. So, the conversations that we would have had six months or a year ago would be different now, just because of the pace of change thats taking place in this environment. The recalcitrance is low to change in AI. And so, its moving faster than Moores Law. It is accelerating as fast as the data allows it. The algorithms themselves have been around for years. Its the ability to capture and use the data that is driving the AI. So, partnering with these capabilities, these technology companies that have access to data thats relevant to your industry is a critical element to being successful.

Laurel: When you do talk to executives about how to be successful with AI, how do advise them if they are behind the competitors and peers in deploying AI?

Cliff: Well, we do surveys like this. We do benchmarks. We harness benchmarks that are out there in other areas and other domains. We look at the pace of change and the relative benefit to that specific industry, and even more narrow than that, the function or the activity within that industry and that business. AI has not infiltrated every single area yet. Its on the way to doing that, but there are areas in customer service, the GNA, the back-office components of an organization, manufacturing, the analytics, the insights, the forecasting, all of that, AI has a strong foothold, so continuing to evolve that. But then there are elements in product design, engineering, other aspects of design that AI is moving into that theres barely a level playing field right now.

So, its uneven. Its very advanced in some areas, its not as advanced in others. I would also say that the perception that will come out in the survey of generalists in these areas may not consider some of the more advanced artificial intelligence capabilities that might be six months, a year, or two years down the road. But those capabilities are evolving very quickly and will be moving into these industries quickly. I would also look at the startup ecosystem as well. The startups are evolving quickly. The technologies that a startup is using and introducing into new industries to disrupt those industries are not necessarily being considered by the more established companies who have existing operating models and existing business models. So, a startup may be using AI and data to totally transform how an industry consumes a product or a service.

Laurel: Thats good advice as always. Cliff, thank you so much for joining us today in what has been a great conversation on the Business Lab.

Cliff: My pleasure. Its great talking to you.

Laurel: That was Cliff Justice, the US leader for enterprise innovation for KPMG, who I spoke with from Cambridge, Massachusetts, the home of MIT and MIT Technology Review, overlooking the Charles River.

Thats it for this episode of Business Lab. Im your host, Laurel Ruma. Im the Director of Insights, the custom publishing division of MIT Technology Review. We were founded in 1899 at the Massachusetts Institute of Technology. And you can find us in print, on the web, and events each year around the world. For more information about us and the show, please check out our website at technologyreview.com.

This show is available wherever you get your podcasts.

If you enjoy this episode, we hope youll take a moment to rate and review us. Business Lab is a production of MIT Technology Review. This episode was produced by Collective Next. Thanks for listening.

This podcast episode was produced by Insights, the custom content arm of MIT Technology Review. It was not produced by MIT Technology Reviews editorial staff.

Read more:

Embracing the rapid pace of AI - MIT Technology Review

Judge Xavier Rodriquez wrote a book review about AI in court in the book "When Machines Can Be Judge, Jury, and Executioner: Justice in the Age of Artificial Intelligence," written by former US District Judge Katherine Bolan Forrest which addresses the growing use of artificial intelligence tools that augment or potentially displace human judgment. In specific, she focuses on AI assessment tools that are used to predict risk and needs assessments, or RNAs, for offenders.The May 18, 2021 book review entitled Judging A Book: Rodriguez Reviews 'When Machines Can Be Judge' included these comments:

These RNA tools are often used to guide judicial decisions on whether to grant a criminal defendant bail or remand, and the duration and conditions of a defendant's incarceration.

Forrest's prior service as a judge, her interest in technology, and her easy-to-read writing style makes for an interesting and understandable introduction to AI as it is currently used in the criminal justice process.

Forrest concludes her book with a discussion of how AI has been deployed in lethal autonomous weapons used by our military forces.

In an approving tone, she notes that these weapons can result in increased identification accuracy, allow for dispassionate decision making, and enable quick decisions about whether to engage a target.

Interesting perspective on AI in the Courtroom!

2021 Foley & Lardner LLPNational Law Review, Volume XI, Number 139

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AI in the Courtroom to predict RNAs for offenders - The National Law Review

The Way of the Future, a church founded by a former Google and Uber engineer, is now a thing of the past.

Its been a few months since the worlds first AI-focused church shuttered its digital doors, and it doesnt look like its founder has any interest in a revival.

But its a pretty safe bet well be seeing more robo-centric religious groups in the future. Perhaps, however, they wont be about worshipping the machines themselves.

The worlds first AI church The Way of the Future, was the brainchild of Anthony Levandowski, a former autonomous vehicle developer who was convicted on 33 counts of theft and attempted theft of trade secrets.

In the wake of his conviction, Levandowski was sentenced to 18 months in prison but his sentence was delayed due to COVID and, before he could be ordered to serve it, former president Donald Trump pardoned him.

[Read more: Trump pardoned the guy who founded the church of AI]

The church, prior to Levandowskis conviction, was founded on the basic principal of preparing for a future where benevolent AI rulers held dominion over humans.

That may sound ridiculous but, based on articles such as this one, it seems like he was saying algorithms would help us to live better lives and wed be better off accepting and preparing for that than fighting against what was best for us.

If you ask me: thats the future of AI and religion, just minus the AI overlords part.

Levandowskis church wasnt as wacky as it might sound. Major religious organizations employ AI at various levels ranging from automaton-style prayer bots, to full on integration of AI-powered enterprise tools.

The Roman Catholic church embraces AI, though with some expected religious caveats. And some Muslim scholars believe the Islamic faith could help free AI technology from its current profit-driven paradigm that places goodness as secondary to profits.

Of course, none of these churches apparently believe that robots will one day deserve our spiritual allegiance as they guide us beyond the mortal coil. But the writing is on the wall for a different kind of AI-powered religious experience.

AI can be a powerful tool due to its ability to surface insights from massive amounts of data. This makes it a prime candidate for religious use, if for no other reason than its a new technology that people still dont quite understand.

In fact, whenever a new paradigm for technology comes along, religious groups tend to spring up in its wake.

When L Ron Hubbard invented the e-meter in 1952, for example, it was based on the pseudoscience technology behind the polygraph. A year later he founded the Church of Scientology.

The Tech is a bedrock of Scientology belief. Though the use of the term specifically seems to address techniques used to propagate the religions ideas, Hubbards writing and speeches tend to embrace technology as an important part of the religion.

Hubbards initial works spanned hundreds of texts, books, and speeches. But the onset of accessible television technology and mass media in the 1960s lead to the founding of Golden Era Productions, a state-of-the-art production facility where, to this day, all of Scientologysvideos are still produced.

Later, in 1974, a pair of UFO enthusiasts founded Heavens Gate, a religious group that was also heavily-influenced by technology throughout its existence.

Originally, the founders told followers a literal spaceship would come for them. But, as technology advanced andpersonal computers and the internetbegan to flourish, the group supported itself by designing websites. Some experts even believe some of the groups beliefs were based on mystical interpretations of computer code.

Both of these groups saw their genesis during periods of technological inflection points. Scientology began in the wake of the second World War. When the war started, many warriors were still fighting on horseback and the RADAR hadnt been invented. By the time WWII was over, technology had advanced to an unrecognizable state.

And Heavens Gate came to prominence just as personal computers and the internet were bringing the most curious, technologically-inclined people together around the globe.

Technology shifts that redefine the general public perception of whats possible tend to spur revolution in all domains and religion is no exception.

AI is a backbone technology. As such, its use by religious groups in the future will likely be as ubiquitous as their use of electricity or the internet.

After all, priests and pastors look things up on Google and chat on Facebook just like the rest of us. Its easy to imagine churches implementing AI stacks in their IT setups to help them with everything from record-keeping to building out chatbots that can surface ecclesiastical documents for parishioners on demand.

But there are other, less technology-basedways AI tech could be employed and, in these cases, the past is prescient.

If we use Scientology as an example, we can see a direct correlation between their e-meters and the modern AI paradigm where machine learning models require a human in the loop to be considered fully-functional.

Per the Church of Scientology,the e-meter device by itself does nothing. Basically, the e-meter is a piece of technology that doesnt work unless someone trained in its spiritual applications wields it.

There are thousands of AI systems that work the exact same way. Developers claim their work can do everything from predict crime using historical police reports to determine if someone is a terrorist from nothing but an image of their face.

Of course, these systems dont actually work. Theyre just like e-meters in that they can be demonstrated to perform a specific function (AI parses data, e-meters measure a small amount of electrical activity in our skin), but that function has nothing to do with what users are told theyre being employed for.

In other words: E-meters dont actually measure anything related to what auditors use them for, theyre much like the EMF meters that ghost hunters use to prove that ghosts exist.

And, in that exact same vein:AI cant tell if youre a terrorist by looking at your face. But it can be trained to label outputdata any way you want it to.

If you think all white men with mustaches are porn stars, you can train an AI to always identify them that way. If you want to label a group of people terrorists, you can train AI to label people who look a certain way as terrorists.

And, since it all happens in a black box, its impossible for developers to explain exactly how they work you simply have to have faith.

It is a demonstrable fact that AI systems and databases are inherently biased. And, to date, billion and trillion dollar-enterprises such as Google, Amazon, Facebook, Microsoft, and OpenAI have yet to come close to solving this problem.

We know these systems dont work, yet some of the most prestigious universities and largest companies in the world use them.

These broken, unfinished systems continue to proliferate because people have faith in them, no matter what the experts say.

We truly do live in a faith-based world when it comes to AI. When Elon Musk takes his hands off the wheel of his Tesla for minutes at a time during a televised interview, hes showing you that a billionaire genius has faith, and hes asking you to believe too.

We know its faith-based because, when it comes to brass tacks, Tesla requires drivers to keep their hands on the wheel and their eyes on the road at all times. Numerous accidents have occurred as a result of consumers misusing Teslas Autopilot and Full Self Driving technologies, and in every case where users took their hands off the wheel, Teslas claimed the driver was responsible.

Apparently, Musks faith in his product endswhere Teslas liability begins.

When facial recognition software companies tell us their products work, we believe them. We take it on faith because theres literally no way to prove the products do what they claim to do. When a facial recognition system gets something wrong, or for that matter, even when they get something right: we cannot know how it came to the result it did because these products do their work inside of a black box.

And, when so-called emotion-recognition systems attempt to predicthuman emotions, motivations, or sentiments, they require a huge leap of faith to believe. This is because we can easily demonstrate they dont function properly when exposed to conditions that dont fall within their particular biases.

Eventually, we hope real researchers and good actors will find a way to convince people that these systems are bunk. But it stands to reason theyre never going away.

They allow businesses to discriminate with impunity, courts to issue demonstrably racist sentences without accountability, and police to practice profiling and skip the warrant process without reprisal. Deep learning systems that make judgements on people allow humans to pass the buck, and as long as there are bigots and misogynists in the world these tools, no matter how poorly they function, will be useful.

On the other hand, its also clear this technology is extremely well-suited for religious use. Where Levandowski understood the power of algorithms as tools for, potentially, helping humans to live a better life, others will surely see a mechanism by which religious subjects can be uniformly informed, observed, and directed.

Whether this results in a positive experience or a negative one would be entirely dependent on how, exactly, religious groups chose to deploy these technologies.

As a simple, low-hanging fruit example, if an e-meter that, by itself, does nothing can become the core technology behind a religious group boasting tens of thousands of people, it stands to reason that deep learning-based emotion recognition systems and other superfluous AI models will certainly wind up in the hands of similar organizations.

When it comes to artificial intelligence technology and religion, Id wager the way of the future is the way of the past.

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The Church of AI is dead... so what's next for robots and religion? - The Next Web

ORLANDO, Fla. & TOKYO(BUSINESS WIRE)Kore.ai, a leading conversational AI software company, has today announced the launch of its AI powered contact center-as-a-service (CCaaS) solution, SmartAssist, in Japanese. SmartAssist will enable enterprises in Japan and Asia-Pacific transform their customer service operations through the use of AI.

Built on Kores no-code Conversational AI platform, SmartAssist provides end-to-end call automation for inbound customer service calls through combination of conversational IVR, virtual assistants and call deflection. Through a simple SIP transfer, SmartAssist deflects calls to appropriate virtual or live assistants. SmartAssist also gives customers automated speech recognition (ASR) and text-to-speech (TTS), making it easier for IVR enabled call centers to enhance their support technology stack.

SmartAssist is backed by Kores multi engine natural language processing (NLP) technology, to automate sophisticated conversations with personalization and relevant context. It also supports omnichannel deployment and remembers the context when the customer shifts from one channel to another in the course of a dialog to ensure a consistent experience. Also, when needed to engage a live agent on the call, SmartAssist passes on all the call history and caller details making it easier for agents to take the call forward.

The Covid-19 pandemic has accelerated the trend toward cloud contact centers and the need for automation and digital customer support. Conversational AI will play a key role in this transformation by driving contact center innovation and improving agent productivity. The Japanese version of SmartAssist will help enterprise customers in this region improve time-to-market and enhance the customer experience in their native language, said Sreeni Unnamatla Executive Vice President Asia Pacific and Japan.

Kore is helping global 2000 enterprises in automating routine business interactions and creating omnichannel experience for their customers. Kore is unique in the conversational AI market in that it allows customers to build virtual assistants through the companys no-code platform and also deploy pre-built virtual assistants for banking, healthcare and functional areas such HR, IT Support, and Sales. Kore differentiates itself through its conversational UX, superior NLP, explainable AI, and no-code unified platform that empowers people to use technology to transform how their business operates.

About Kore.ai

Kore increases the speed of business by automating customer and employee interactions through digital virtual assistants built on its market-leading conversational AI platform. Companies who prioritize customer and employee experience use Kores no-code conversational AI platform to raise NPS and lower operational costs. The top 4 banks, top 3 healthcare businesses, and over 100 Fortune 500 companies have automated a billion interactions since Kore was founded in 2015, and its pre-built industry and functional virtual assistants have made it easier and faster for these top-performing businesses to scale the impact of front office automation. Kore has been recognized as a leader by top analysts and ensures the success of its customers through a growing team headquartered in Orlando with offices in India, the UK, Japan, and Europe.

Visit kore.ai to learn more.

Contacts

Media ContactsKarthik G

karthik.gandrajupalli@kore.com

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Kore.ai Launches SmartAssist in Japanese to Deliver AI-powered Call Center Automation - TechDecisions

To conclude that AI serving as an aid to human thinking is necessarily better than possibly replacing some aspects of human decision making, when we simply dont yet have the technological capability to test one over the other, would fall into the logical fallacy of equating a presumption with a conclusion.

In response to our recent article on artificial intelligence (AI) reducing transactional costs to help determine infringement and invalidity determinations, a commenter made an interesting counterpoint, paraphrased as the following: AI provides useful tools that should be used as an aid to human thinkers, not as a replacement to human thinking. Moreover, when it comes to AI making subjective determinations, such as obviousness or novelty, we should be skeptical of relying on AI, either legally or practically.

We appreciate the counterpoint and we wanted to address it in this follow-up article.

How do we determine the best role for AI in our patent system? If we have a choice between AI serving as an aid to human thinking, or possibly replacing some aspects of human decision making, what is the correct choice? What would better serve to improve our patent system?

To have a productive discussion re AIs proper place in our patent system, we need to first understand what improvement to our patent system means. When we have common ground as to how to assess improvement, only then we can discuss which role of AI would better implement that improvement.

To frame our understanding of such improvement, let us look at the patent system as it stands today.

The judiciary sits in the middle of the patent-transaction ecosystem. When presented with a case, the judiciary performs a two-step process. It takes on the role as (1) the arbiter of factual and legal contentions between parties, and (2) the enforcer of its ultimate decision. In its role as arbiter, the court determines informational attributes relating to patent validity, scope, and infringement. After this determination is made, it then enforces that decision.

In this system, the presumption is that the basic informational attributes of a patent are either unknown or at best contested, and we need the court system to make this determination.

Herein lies the problem with our patent system. Relying on the court to determine basic informational attributes of a patent is both costly and inefficient. It costs millions of dollars and takes several years to determine whether the patent is valid, infringed, and the damages.

Because the court is so inefficient at making these informational determinations regarding a patent, enforcement costs in turn are extremely high.

Further, these information and enforcement costs are intermingled; meaning, you cannot enforce a patent unless the same court system first determines the basic informational attributes of a patent, resulting in a costly self-perpetuating cycle of inefficiency.

Why is this important to understand when framing a discussion about the patent system and patent transactions within that system?

As Douglass C. North pointed out in his 1992 paper Transaction Costs, Institutions, and Economic Performance, the framework of our patent system creates the actors that operate within it.

The constraints imposed by the institutional framework (together with the other standard constraints of economics) define the opportunity set and therefore the kind of organizations that will come into existence.

North gave a very powerful example:

If the highest rates of return in a society are from piracy, then organizations will invest in knowledge and skills that will make them better pirates; if the payoffs are highest from increasing productivity, then firms and other organizations will invest in skills and knowledge that achieve that objective.

In our present-day patent system, extremely high informational costs create the economic driver to reduce enforcement and bargaining costs between parties in a patent transaction.

Put another way, the court systems high informational cost structure creates a driver to minimize enforcement costs, which manifests in todays patent litigation as early settlements that are below the cost of determining the informational attributes of a patent.

Put yet another way, the court systems high informational cost structure creates the economic driver for low-value and nuisance patent litigation (see part I and part II of an analysis relating to how we have historically misdirected patent policy to deter such nuisance patent litigation).

North recognized the central role of informational costs: [t]he cost of transacting arises because information is costly and held asymmetrically by the parties to exchange.

In a perfect patent system, the informational attributes of a patent are efficient to determine and known to both parties. When informational costs are low and informational attributes are known to both parties, the following occurs:

North describes this as the zero-cost transaction. This is a perfect system in which there are no transactional costs between a patent holder and an alleged infringer reaching an agreement on a patent transaction. The only money spent, if any, is for the value of a patent license.

So, when we are thinking about patent reform, the discussion should be centered on how do we approach a zero-cost transaction for patent transactions? This sets the standard for improvement.

Assuming we are on the same page regarding what it means to improve our patent system, this frames the next question: between (1) AI serving as an aid to human thinking, or (2) possibly replacing some aspects of human decision making, which of the two better serves to improve our patent system?

At this point, I dont believe we can actually answer that question, because we dont live in a world where AI can reliably replace aspects of human thinking with respect to our patent system.

But to conclude one is necessarily better than the other, when we simply dont have the technological capability to test one over the other, would fall into the logical fallacy of equating a presumption with a conclusion.

Instead, North would offer a different approach. He described characteristics of successful institutions. Namely, institutions that allow for decentralized decision-making and trial and error see greater success over time.

Therefore, institutions should encourage trials and eliminate errors. A logical corollary is decentralized decision making that will a society to explore many alternative ways to solve problems.

Applying Norths teachings to our patent system, he would recommend we test different methodologies to determine informational attributes of a patent and learn through trial and error which methodology best reduces informational costs. Only when we have the opportunity to apply and test different methodologies to determine informational attributes of a patent will we truly learn which method is best.

North certainly factored in the use of technology and technologys role in an institution:

Institutions, together with the technology employed, affect economic performance by determination transaction and transformation (production) costs.

Relying on the teachings of North, we should actively test AI in different applications and scenarios and determine which would allow us to approach a zero-cost transaction, particularly zero costs to determine the informational attributes of a patent.

But to enable us to test AI effectively, we cannot foreclose ourselves to the possibility that AIs proper place could be to actually replace some aspects of human thinking.

If AI replacing human decision making in certain circumstances would enable a zero-cost patent transaction, then this may be the proper place for AI in the patent system. But if using AI as a mere tool to aid human thinking enables us to approach this zero-cost transaction, then this may instead be the best role for AI.

In essence, lets not put the cart before the horse when making determinations regarding AIs proper role in our patent system. To improve our patent system, we need to come to common understanding on the key problem it faces, namely, its unsound economic underpinnings. And we need to allow ourselves greater flexibility to test different methods and technology to improve the patent system by helping us to eliminate, or at least significantly reduce, the high costs and inefficiencies of determining the informational attributes of a patent.

Gau Bodepudi Is the Managing Director at and co-founder of IP EDGE LLC. He has more than 12 years experience in all aspects of patent management and monetization, including strategic prosecution, litigation, licensing, brokering, and portfolio management within various technological fields such as ecommerce, consumer electronics, networking, financial services, mobile communications, and automotive technologies. Mr. Bodepudi also created a patent monetization blog, InvestInIP.com, where he writes on patent reform and policy

Eesha Kumar is an intern at IP EDGE LLC. She graduated with a bachelors degree in political science from The University of Georgia and is planning on attending law school.

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We Must Remain Open to the Future Possibilities of AIEven if it Means Replacing Humans - IPWatchdog.com

Yesterday at Googles I/O developer conference, the company outlined ambitious plans for its future built on a foundation of advanced language AI. These systems, said Google CEO Sundar Pichai, will let users find information and organize their lives by having natural conversations with computers. All you need to do is speak, and the machine will answer.

But for many in the AI community, there was a notable absence in this conversation: Googles response to its own research examining the dangers of such systems.

In December 2020 and February 2021, Google first fired Timnit Gebru and then Margaret Mitchell, co-leads of its Ethical AI team. The story of their departure is complex but was triggered by a paper the pair co-authored (with researchers outside Google) examining risks associated with the language models Google now presents as key to its future. As the paper and other critiques note, these AI systems are prone to a number of faults, including the generation of abusive and racist language; the encoding of racial and gender bias through speech; and a general inability to sort fact from fiction. For many in the AI world, Googles firing of Gebru and Mitchell amounted to censorship of their work.

For some viewers, as Pichai outlined how Googles AI models would always be designed with fairness, accuracy, safety, and privacy at heart, the disparity between the companys words and actions raised questions about its ability to safeguard this technology.

Google just featured LaMDA a new large language model at I/O, tweeted Meredith Whittaker, an AI fairness researcher and co-founder of the AI Now Institute. This is an indicator of its strategic importance to the Co. Teams spend months preping these announcements. Tl;dr this plan was in place when Google fired Timnit + tried to stifle her+ research critiquing this approach.

Gebru herself tweeted, This is what is called ethics washing referring to the tech industrys tendency to trumpet ethical concerns while ignoring findings that hinder companies ability to make a profit.

Speaking to The Verge, Emily Bender, a professor at the University of Washington who co-authored the paper with Gebru and Mitchell, said Googles presentation didnt in any way assuage her concerns about the companys ability to make such technology safe.

From the blog post [discussing LaMDA] and given the history, I do not have confidence that Google is actually being careful about any of the risks we raised in the paper, said Bender. For one thing, they fired two of the authors of that paper, nominally over the paper. If the issues we raise were ones they were facing head on, then they deliberately deprived themselves of highly relevant expertise towards that task.

In its blog post on LaMDA, Google highlights a number of these issues and stresses that its work needs more development. Language might be one of humanitys greatest tools, but like all tools it can be misused, writes senior research director Zoubin Ghahramani and product management VP Eli Collins. Models trained on language can propagate that misuse for instance, by internalizing biases, mirroring hateful speech, or replicating misleading information.

But Bender says the company is obfuscating the problems and needs to be clearer about how its tackling them. For example, she notes that Google refers to vetting the language used to train models like LaMDA but doesnt give any detail about what this process looks like. Id very much like to know about the vetting process (or lack thereof), says Bender.

It was only after the presentation that Google made any reference to its AI ethics unit at all, in a CNET interview with Google AI chief Jeff Dean. Dean noted that Google had suffered a real reputational hit from the firings something The Verge has previously reported but that the company had to move past these events. We are not shy of criticism of our own products, Dean told CNET. As long as its done with a lens towards facts and appropriate treatment of the broad set of work were doing in this space, but also to address some of these issues.

For critics of the company, though, the conversation needs to be much more open than this.

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Google made AI language the centerpiece of I/O while ignoring its troubled past at the company - The Verge

Modern chic business people working in an incredible futuristic & original office space

Society has long been fascinated by artificial intelligence. Countless movies have been made about robots taking over the world, and while they make for good entertainment, its just not realistic.

This fear is far from being founded in truth. In fact, AI is actually helping us keep our jobs.

The direction artificial intelligence is taking as an emerging technology offers us new degrees of efficiency and productivity we have not been able to achieve before. Companies are using AI to streamline everyday workplace tasks and transform the client experience. Whether youre a CEO or an entry-level employee, artificial intelligence should be the next tool on your radar because it will keep you competitive in your industry.

Dont worry about how artificial intelligence is affecting the job market - embrace it. LivePerson, a leading technology company making new waves in the artificial intelligence world, is helping brands leverage AI technology to engage with their customers virtually. Through the use of AI, client support jobs are becoming more conversational and more essential.

Employees and AI Can Partner to Create a More Efficient Workforce

Since the onset of the pandemic, brands have relied almost solely on virtual communication to communicate with their customers. But no one wants to wait on hold, listening to elevator music while trying to resolve an issue or process a return. Bots are swiftly becoming the new standard in customer engagement, offering brands a new, more efficient way to connect with their consumers.

Your first thought may be that AI is taking over these types of client support jobs, but really its only improving them and making the role more significant to brands. Heres an example: remember how the Covid-19 pandemic picked up right at the beginning of wedding season here in the United States? This was difficult for many brides and wedding-centric brands.

For example, LivePerson created a bot for the brand Davids Bridal to help them connect with consumers and forge meaningful interactions. The bot named Zoey helped filter and sort client requests so that human customer engagement representatives could provide the best help possible. It also expanded call center employees capabilities as they were able to learn how to manage the customer experience through a new technology. Artificial intelligence works together with employees to promote customer satisfaction by reducing the length of wait times, and providing the customer with an immediate response to their inquiry.

Connected Customers are Happy Customers

Customers ultimately want ease of use and immediacy of response when connecting with brands online. They dont want these interactions to be cumbersome. Perhaps a consumer wants to get a status update about an order, or perhaps they want a customer engagement representative to explain the steps for rebooting a system.

Bots help customer engagement teams connect with customers in the most meaningful way by filtering out questions that are easiest to answer and leaving more time for representatives to connect directly with customers who have highly complex requests.

As another example, LivePerson created a system for GoDaddy that helped them promote customer satisfaction before and during the pandemic. Through a thorough analysis, they found that they were missing opportunities for customer engagement because they could not offer their customers guidance via the web. This in mind, they worked to create a conversational AI system that helped filter out smaller requests from more complex ones.

The technology quickly proved to be a success. The company witnessed 200% in monthly and YOY messaging contacts, which led to a 52% increase in revenue per YOY contact. Ultimately, this led to the overall success of the customer engagement team because they were able to cultivate meaningful relationships with customers and focus on those high complexity, high value interactions instead of the ones bots could handle on their own.

Artificial intelligence is changing how we look at the customer experience. Its not replacing the human aspect of customer engagement, but instead it is increasing consumers desire to connect conversationally with brands. Technology like conversational AI helps brands reach their customers, and only makes the role of customer engagement representatives more essential, more streamlined, and more important.

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The Future Workforce: How Conversational AI Is Changing The Game - Forbes

Before diving into cyber security and how the industry is using AI at this point, lets define the term AI first. Artificial Intelligence (AI), as the term is used today, is the overarching concept covering machine learning (supervised, including Deep Learning, and unsupervised), as well as other algorithmic approaches that are more than just simple statistics. These other algorithms include the fields of natural language processing (NLP), natural language understanding (NLU), reinforcement learning, and knowledge representation. These are the most relevant approaches in cyber security.

Given this definition, how evolved are cyber security products when it comes to using AI and ML?

I do see more and more cyber security companies leverage ML and AI in some way. The question is to what degree. I have written before about the dangers of algorithms. Its gotten too easy for any software engineer to play a data scientist. Its as easy as downloading a library and calling the .start() function. The challenge lies in the fact that the engineer often has no idea what just happened within the algorithm and how to correctly use it. Does the algorithm work with non normally distributed data? What about normalizing the data before inputting it into the algorithm? How should the results be interpreted? I gave a talk at BlackHat where I showed what happens when we dont know what an algorithm is doing.

So, the mere fact that a company is using AI or ML in their product is not a good indicator of the product actually doing something smart. On the contrary, most companies I have looked at that claimed to use AI for some core capability are doing it wrong in some way, shape or form. To be fair, there are some companies that stick to the right principles, hire actual data scientists, apply algorithms correctly, and interpret the data correctly.

Generally, I see the correct application of AI in the supervised machine learning camp where there is a lot of labeled data available: malware detection (telling benign binaries from malware),malware classification (attributing malware to some malware family), document and Web site classification, document analysis, and natural language understanding for phishing and BEC detection.There is some early but promising work being done on graph (or social network) analytics for communication analysis. But you need a lot of data and contextual information that is not easy to get your hands on.Then, there are a couple ofcompanies that are using belief networks to model expert knowledge, forexample, for event triage or insider threat detection. But unfortunately, these companies are a dime a dozen.

That leads us into the next question: What are the top use-cases for AI in security?

I am personally excited about a couple of areas that I think are showing quite some promise to advance the cyber security efforts:

Given the above it doesnt look like we have made a lot of progress in AI for security. Why is that? Id attribute it to a few things:

Is there anything that the security buyer should be doing differently to incentivize security vendors to do better in AI?

I dont think the security buyer is to blame for anything. The buyer shouldnt have to know anything about how security products work. The products should do what they claim they do and do that well. I think thats one of the mortal sins of the security industry: building products that are too complex. As Ron Rivest said on a panel the other day: Complexity is the enemy of security.

*** This is a Security Bloggers Network syndicated blog from Artificial Intelligence and Big Data in Cyber Security | raffy.ch Blog authored by Raffael Marty. Read the original post at: http://feedproxy.google.com/~r/RaffysComputerSecurityBlog/~3/CBkWKAnpz24/

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Taking Inventory Where Do We Stand With AI and ML in Cyber Security? - Security Boulevard

Here are the top postings in artificial intelligence, with 9 out of 10 coming with six-figure salaries, according to Indeed.

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As the rise of artificial intelligence continues to impact the workplace, with many employees fearing they may be eventually replaced, jobs in this advanced technology are more sought-after than ever. A new report from Indeed highlights the most in-demand jobs in AI, as well as the salaries that come with these positions.

To create this list, Indeed took a look at job postings, by percentage that included an "AI" term, between February 2021 and April 2021. These "AI" definitions included the following phrases: "artificial intelligence," "ai engineer," "ai research," "ai scientist," "ai developer," "ai technica," "ai programmer," "ai architect," "machine learning," "ml engineer," "ml research," "ml scientist," "ml developer," "ml technical," "ml programmer," "ml architect," "natural language processing," "nlp," and "deep learning." Then Indeed figured out the average salary of these positions, incorporating their reported salary information over a period from May 2019 through April 2021.

SEE: Digital Transformation: A CXO"s guide (ZDNet/TechRepublic special feature) | Download the free PDF version (TechRepublic)

Here are the top 10 jobs in AI, along with their salaries, according to Indeed:

Data scientist: $110,000

Senior software engineer: $120,000

Machine learning engineer: $125,000

Data engineer: $122,060

Software engineer: $100,000

Software developer: $95,000

Software architect: $135,107

Senior data scientist: $127,500

Full stack developer: $108,730

Principal software engineer: $155,000

Instead of worrying about AI replacing us, the data encourages a positive outlook about the technology, showing that developing sophisticated AI has been good for job creation, and that advanced technology may make room for new, higher-level roles for employees. In a recent survey by Citrix, previously reported on at TechRepublic, 82% of leaders and 44% of employees surveyed expect AI to create the new role of "Robot/AI trainer" in the future. And 77% of professionals said that they believe that AI will help reduce the time of decision-making at work, by 2035, and 83% of those surveyed believe that it will eliminate the need for low-level tasks.

Additionally, 82% of leaders surveyed by Citrix said that the AI boom will mean that organizations create the role of "Chief of Artificial Intelligence" by 2035, and will also institute an AI department that oversees how the business can integrate and oversee AI operations.

For those interested in pursuing a career in AI, TechRepublic has also previously reported on the best places to find a job in AIincluding California, Virginia and Washington.

Indeed has also expected that 2021 will bring good news for hiring, in general, reporting that 27% of employers plan to hire more workers now, versus pre-pandemic.

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The top 10 AI jobs in America - TechRepublic