Category Archives: Ai

A documentary on the late chef Anthony Bourdain showcases the extraordinary advances of artificial intelligence (AI). Roadrunner uses Deep Fake technology to show Bourdain mouthing words he never uttered. Its a wake-up call on potential side effects of AI. Deep Fakes are a subfield of AI that allow realistic forgeries of both video and audio. The speed of advances in AI have made it possible to create Deep Fakes using freely available software and computer processing power that can be rented. AI is perhaps the most transformative technology under development. Consequently, it also brings about entirely new risks.

Deep Fakes pose a fundamental danger. They can quickly undermine trust, the invisible bond that holds many collectives together. There are many examples of Deep Fakes being used by state actors to influence elections and sow seeds of discord. The US has been a victim of Deep Fakes. Even in India, Deep Fakes are known to have been circulated in some electoral contests. AIs rapid upgrades, coupled with a transition to digital modes of governance, calls for a new level of safeguards on the part of GoI.

One example is the way the US is gearing up to face new challenges. Its been hit repeatedly by ransomware attacks, where hackers introduce malicious software code into networks to prevent victims from accessing their data. This year an oil pipeline operator in the US was hit by ransomware and was forced to pay the hackers. American responses are no longer reactive. The US is investing in talent and technology needed to cripple hackers cyber infrastructure. Such enormous challenges mean GoI must build on its existing technology talent pool. Penny-pinching and red tape shouldnt come in the way. Social and economic costs of unchecked Deep Fakes and ransomware will be far greater.

This piece appeared as an editorial opinion in the print edition of The Times of India.

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AIs dark side: Deep Fakes are easy to create. GoI needs to invest in tech & talent to counter the new dange - The Times of India Blog

[author: Doug Austin, Editor of eDiscovery Today]

The final installment of the virtual Legalweek(year) just concluded, with the fifth installment having completed this week, after previous iterations in February (the traditional time of year for in-person Legalweek), March, April, and May. I attended a handful of sessions and planned to give you a sampling of the sessions I attended, but the session that I attended at the end of the conference on Wednesday was so good, I decided to cover it specifically.

The session was The Ethics of AI in The Legal Profession and it was conducted by Tess Blair of Morgan Lewis and Maura R. Grossman of the University of Waterloo and Maura Grossman Law (who should be a familiar name to any of you who understand Technology Assisted Review (TAR) as she and Gordon V. Cormack defined the term and issued the groundbreaking study that demonstrated how TAR could be more efficient and effective for document review).

Blair and Grossman covered several aspects of the use of AI, a couple of which I will briefly recap here. They also provided some interesting graphics to illustrate various concepts such as machine learning (interspersed pictures of chihuahuas and blueberry muffins so similar its startling), natural language processing (NLP) and deep learning.

Advising Clients Developing or Using AI

Among the topics covered here were the idea of how crowdsourcing can introduce bias into AI algorithms, where the example used was to type in the phrase lawyers are into Google search and the completion terms that dropped down included terms like scum, liars, sharks, evil, and crooks. Ouch!

There are three places where AI bias can come into play: 1) the data, the example used was to use only white faces to train an algorithm to the point it wont handle black faces; 2) the algorithm, which can be tuned to weight things differently; and 3) humans, which may have an algorithm aversion, may have automation bias (where the algorithm is assumed to be correct because it is not human) or may have confirmation bias (where they agree only if the results confirm what they already believe).

Grossman also discussed the use of the Correctional Offender Management Profiling for Alternative Sanctions (COMPAS), which tended to rate the possibility for black defendants to reoffend at much higher score than non-black defendants, as was the case of an 18 year old black woman who was rated high risk (a score of 8) for future crime after she and a friend took a kids bike and scooter that were sitting outside whereas a 41 year old white man who had already been convicted of armed robbery and attempted armed robbery was rated a low risk (according to this article by ProPublica).

Grossman pointed out that COMPAS experienced function creep where it was originally designed to provide insight into the types of treatment an offender might need (e.g., drug or mental health treatment), then expanded to decision making about conditions of release after arrest (e.g., release with no bail, bail, or retention without bail), before being expanded again to decisions about sentencing.

Blair added discussions regarding privacy and also AI moral dilemmas, such as the case of an autonomous vehicle, entering a tunnel with child in the middle of the road and a decision to make whether to go straight ahead and kill the child or veer off into the wall and kill the passenger (yikes!). Those and other moral dilemmas can be found here at MITs Moral Machine site.

Resources for Practicing with AI

With regard to practicing law while using AI, the presenters discussed several sources of guidance with regard to an attorneys duty for understanding AI, including:

Conclusion

Blair and Grossman concluded with a brief discussion on whether AI is going to take lawyer jobs (there may be fewer attorneys in the future, but they will be more focused on the types of tasks they were trained for in law school) and whether AI will ever become smarter than humans (Grossman stated that the technology often still cant do what a 3-year-old can do).

So, with great power comes great responsibility.

But lawyers (or anybody using AI) need to do their part to understand the technology and the concepts (as well as the risks) to fully benefit from AI. When they do, they can accomplish amazing things!

Next year, Legalweek returns to an in-person event in New York City from January 31 from February 3! See you there!

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The Ethics of AI in The Legal Profession | Ipro Tech - JDSupra - JD Supra

As more fast food restaurants continue to experiment with plant-based protein alternatives - Little Caesars 'Planteroni' Pizza in a partnership with Field Roast being one of the most recent examples plenty of consumer still havent bought into the fake meat craze.

Part of the problem behind consumers stubborn resistance to adopting more of a plant-based or flexitarian diet is the common, and fair, complaint that many of these meat alternatives taste off. Soy-based patties can leave an unpleasant aftertaste, and never mind the crumbly texture. Beyond Meats fake chicken of yesteryear offered notes of carrots, frozen peas and fava beans, and not necessarily in a good way. And while Beyonds and Impossible Foods plant-based protein substitutions for burgers are about the closest thing one can get to the real thing, the coconut oil can result in traces of sweetness that can be off-putting. Of course, all of these are improvements over the veggie burgers from many years ago, which would have tasted far better if they were allowed to remain as vegetables.

But what if flavorings could help make these new and futureplant-based proteinproducts more palatable to more consumers? After all, the companies that are driving the multi-billion dollar global flavor and fragrance industry keep developing ingredients that are increasingly more sophisticated and are in just about in every product we put in or on us. Many of us are already taking such action in our kitchens vanilla extract, for example, is a common ingredient in our cupboards because it easily binds to proteins without giving off its flavoring and it masks other flavors that could otherwise taste unpleasant.

Now, lets add another challenge, and one that if overcome, could help plant-based protein scale up: which of course would help wean more consumers away from the carbon- and water-intensive meat and dairy industry.

One hurdle that food companies face is thatdevelopingnew products that will eventually be accepted by the masses can take years. But what if that process could be shortened by harnessing the potential of artificial intelligence (AI)?

To that end, Firmenich, the Switzerland-based fragrance and flavoring giant, says its on a path toward improving the taste of plant-based protein products.

The company recently announced the launch of what it says is the first flavor developed by AI, a lightly grilled beef taste that could be used in plant-based foods. Emphasis should be put on lightly grilled, as yet another complaint of fake meat is that no matter how its cooked, it can often leave dinerswith a cringeworthy charred taste.

Firmenich is understandably mum on what flavor notes are exactly in this new AI-induced flavor profile. Nevertheless, the company could provide that final piece of the puzzle to companies that seek to recreate the flavor and texture of meat: and not 95 percent, not 99 percent, but a 100 percent success in copying meats flavor profile. So far, companies like Beyond and Impossible Foods have pretty much nailed the texture part. What has proven to be difficult is finding that complexity behind the flavor of meat, which includes countless factors such as umami, fat and of course, how it was cooked, such as on a flame or in an oven. And, as we all know if we dont follow directions, the way in which we cook our foods can affect the flavor: so, these plant-based foods need to hold their flavor profile as much as possible whether they are grilled, baked, toasted or out of desperation (ew!) even microwaved.

A company like Firmenich has the advantage of access to a wide range of flavorings within its labs. What it does not have is the time to test out the infinite number of flavor combinations. Therein lies the power of AI the company recently described its database of flavorings to Phys.org as a piano with 5,000 keys that through algorithms allows its staff to test out different combinations. The results could include even more plant-based options coming soon to a supermarket near you.

Image credit: Rolande PG/Unsplash

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How AI Could Improve the Taste of Plant-Based Protein - Triple Pundit

Cloud contact center provider Five9 this week announcedthat it has revamped its intelligent virtual agent (IVA) development platform, added pre-built IVA applications for the health vertical, and tapped partners for integration of voice biometrics, real-time speech analytics, and agent coaching.

These updates reflect the desire Five9 sees among its customers to increase their use of conversational self-service along with live agent support, Callan Schebella, EVP of product management at Five9, shared with No Jitter via email. Five9 sees enormous demand for IVAs as customers seek to increase automation rates to manage costs while improving the customer experience, he added.

IVA Development, Tasks, Integrations

To improve performance and enable development of new capabilities, Five9 has rearchitected and rewritten the underlying layers of Inference Studio, its no-code IVA development platform, andalso provides access to a wide range of conversational technologies from leading companies like Amazon Web Services, Google Cloud, IBM, and others, Schebella said.

That rearchitecting work included creating a media server to support VoiceXML and enable call controls, for delivery of new features like call recording, passive voice biometrics, and more, he added.

Using Studios visual, browser-based interface, developers can design IVA applications by dragging and dropping nodes onto a canvas to build a task flow, Schebella said. As part of the update, Five9 has optimized the interfaces task flow editor for larger canvases that can support more nodes thus reducing the load time when building task flows. Additionally, it has applied reverse indexing to datastores, for faster loading of data, he said.

Other Studio platform enhancements include a new user interface design, a customized development process for messaging applications, and improved reporting and maintenance of IVA tasks and call flows via new dashboard, as shown below. The Studio dashboard is similar to a contact center wall board, where you can monitor IVA usage, call volumes, and chat volumes in real time, Schebella said.

Studio simplifies IVA development even further by providing access to a Task Library, which functions similarly to an app store and contains over 40 pre-built IVA application templates from Five9 and partners that organizations can use as blueprints for their own customized IVAs. With this weeks announcement, Five9 has added its first set of vertical-specific tasks to the library, for healthcare and health insurance providers. Tasks for other verticals, such as retail and financial services, will follow, Five9 said.

The health industry vertical suite includes tasks for appointment scheduling, health plan enrollment, FAQ, prescription management, and test results. To describe how an organization might customize one of these tasks, Schebella shared how it might add its own greeting, prompts, and set of frequently asked questions and answers to the FAQ templates pre-configured task flow. It might also choose preferred text-to-speech voices and languages, he added.

Voice AI Partners

Join Five9 this fall for Enterprise Connect 2021, and catch up on the latest trends and technologies for communications and collaboration, including CCaaS platforms. As a No Jitter reader, use the code NJAL200 to save $200 on your registration.

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Five9 Brushes Up on AI in CCaaS Platform - No Jitter

Leveraging artificial intelligence (AI) provides companies with a unique and enduring competitive advantage, witnessed by the fact that AI-first companies are the worlds only trillion-dollar companies. Thats the view of Ash Fontana, a leading global start-up investor with a specialist interest in artificial intelligence and author of The AI First, How to Compete and Win with Artificial Intelligence.

AI is the one that compounds most quickly and is the hardest to catch up to. Once you build it, it becomes a loop and it builds itself which is why it is so powerful, he tells The Irish Times.

In the book, Fontana frames AI as the third wave of economic development.

The first wave, the physical, dates back to the Stone Age. Think rope traps and spears, tools that allowed us to go beyond our immediate physical reach to gather more food than we could with our bare hands. However, this physical leverage was limited by scale and our intellectual capacity.

The second wave brought intellectual leverage. It started with the printing press which allowed us to distribute information, with computers following, extending our intellectual reach. Insights were limited, however. Artificial intelligence, the third wave, provides decision-making leverage, he says.

Making fabrics

Fontana uses the example of producing fabrics to illustrate the point. The physical age brought the loom which made it possible to stitch fabrics faster than by hand. The information age allowed computers to turn drawings into patterns for the loom to weave.

The third wave changes the game: computers scan photos on social media, figure out consumer trends, draw up new styles and turn drawings into patterns for the loom. New styles hit the stores just as they become fashionable.

In the world of AI, the collection of data is merely the starting point. Deploy the right network of interlocking datasets, filters and tools and you can develop a flywheel effect.

Consider the vast amount of money Google invests in its Google Maps for instance or Amazon on its Alexa-based models, both of which hoover up huge amounts of useful information. As Fontana notes, these are not stand-alone products for either company but part of a suite of products that helps fulfil their data strategy.

Put simply, the power of AI lies in its capacity to turn data into really useful information to aid decision-making. Big tech does it at scale with sometimes alarming efficiency, but Fontana says small businesses can use it too. A sandwich shop can use simple AI tools and techniques to monitor its inventory on the shelves during the day and recalibrate its prices.

In his book he also explains how a lean version of AI can be employed by almost any business.

Many businesses have AI working in the background, whether they are conscious of it or not. Payment service providers such as banks and credit card companies employ it to detect fraud, for example.

Consider the example of point-of-sale solutions group Square. It has the capacity to access real-time information from the tills of its retail customers with their permission which can be used to inform decisions made by its lending subsidiary, Square Capital. By comparing the data from one retail customer to a range of till information from other similar customers it has already lent to, it can predict whether a customer is good for a loan.

You dont get a loan if you dont add data and you qualify for a loan because your data can be compared with other data to make a prediction.

Success in becoming an AI-first company, he says, is about how effectively you can collect the right information and use it to create good predictive models. Master this and you create powerful network effects to outpace your competitors.

First organise the data you have. Spend the time and money you need to get everyone storing data in the right place, with all the tags needed to show context around any particular dimension of the data. Then try some of the more basic machine learning methods available in free easy-to use software packages, he advises.

Fontana distinguishes between entry level and what he calls next level network effects. A simple entry level effect, for example, might tell you that half of your customers are women over 45.

The next level is when the machine is automatically learning over lots of data points throughout a network and is then also creating predictive information We think the next person to buy your product will have the following attributes ...

The most common mistake when trying to become an AI-first company is not having everyone aligned with an AI strategy, he says, in other words, not thinking about where to get data, how to process it into information and build models that generate data network effects.

The next most common mistake is not investing enough in security, infrastructure and governance.

AI is already being deployed as a management tool in traditional offices and work-from-home settings as a way of measuring productivity and possibly engagement, with obvious concerns on the part of some employees.

It is also being using used as an active listening tool. For example, calls to a customer service centre can be monitored to determine customer pain points. Insights from this information can then be incorporated into the design of new products and services.

Fontana says the sci-fi nightmare of rogue robots and supercomputers remains very much in the realm of fiction. We are not at the point where machines are doing things that humans are not ultimately controlling.

AI can be weaponised, but I think ultimately thats about how humans behave and not AI doing something by itself.

Ethical concerns about surveillance capitalism by big tech abound but Fontana argues that AI can be a force for wider good. Consider its role in combatting the Covid 19-pandemic, he says. The development of vaccines and the choice of the most efficacious drugs to treat Covid-infected patients was accelerated through AI while the complex logistical challenges of the mass rollout of vaccines was also helped by the use of AI.

Panel: Principles of Lean AI

Distinguish Lean AI from Lean start-ups: In a start-up, you are looking to develop a minimum viable product. In Lean AI, you are attempting to make an existing model more accurate. The output is a prediction, not a calculation.

Make better predictions: The aim is to get to a point where the prediction starts getting better than a humans. A prediction often takes the form of a classification, for example classifying the information available in a photograph.

Be selective: Collecting data and performing calculations can be expensive. Spend time figuring out exactly what you want to predict in order to settle on the prediction usability threshold, the point at which a prediction becomes useful.

Play with statistics: Get one answer with one statistical method then use that to discover the next answer using another statistical method.

Be clear on your aim: Lean AI is about solving a specific problem with AI and building a small but complete AI-first product that can either grow into other domains or remain focused on one.

The AI-First Company, how to compete and win with artificial intelligence, by Ash Fontana is published by Penguin/Portfolio

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How companies can use AI to get ahead of the competition - The Irish Times

China along with the United States and partners are all hoping to come out on top when it comes to the mastery and application of artificial intelligence. But the Defense Department and its partners don't just aim to be masters of AI, they aim to do it ethically, said the secretary of defense.

"China's leaders have made clear they intend to be globally dominant in AI by the year 2030," Secretary of Defense Lloyd J. Austin III said during remarks to the National Security Commission on Artificial Intelligence. "Beijing already talks about using AI for a range of missions from surveillance to cyberattacks to autonomous weapons."

The U.S. military has its sights on the same target, Austin said. But its approach is going to be different.

"In the AI realm as in many others, we understand that China is our pacing challenge," he said. "We're going to compete to win, but we're going to do it the right way. We're not going to cut corners on safety, security or ethics. And our watchwords are 'responsibility' and 'results.' And we don't believe for a minute that we have to sacrifice one for the other."

The department's "responsible AI" effort, Austin said, is at the center of ensuring the DOD does AI the right way.

"Responsible AI is the place where cutting-edge tech meets timeless values. You see, we don't believe that we need to choose between them, and we don't believe doing so would work," he said. "Our use of AI must reinforce our democratic values, protect our rights, ensure our safety, and defend our privacy."

The Defense Department's use of AI, Austin said, will enhance its military operations, which is why those efforts are being pursued.

"But nothing is going to change America's commitment to the laws of war and the principles of our democracy," he said.

We're going to compete to win, but we're going to do it the right way. We're not going to cut corners on safety, security or ethics.''

Right now in the department, Austin said, there are more than 600 efforts underway to enhance the nation's defense using artificial intelligence.

"[That is] significantly more than just a year ago," he said. "And that includes the Artificial Intelligence and Data Acceleration initiative, which brings AI to bear on operational data."

Also included there is Project Salus, which began in March 2020 in partnership with the National Guard, Austin said. Project Salus used artificial intelligence to help predict shortages for things like water, medicine and supplies used in the COVID fight.

Also included in the current AI efforts is the Pathfinder Project, which Austin said is an algorithm-driven system to help the department better detect airborne threats by using AI to fuse data from military, commercial and government sensors in real time.

Increasing the department's AI capability and providing tools to better enable warfighters will mean getting the right people on board to make it happen, Austin said. That's not just civilian experts on the topic; it means service members, as well, he said.

Austin said DOD is going to have to do a lot better at recruiting, training and retaining talented people which are often young people who can lead the department into and through the AI revolution. "That means creating new career paths and new incentives. And it means including tech skills as a part of basic-training programs."

Emerging technologies, he said, are going to be at the center of the department's strategic development, Austin said, and the department must overcome its ingrained culture of risk aversion.

"We need to smarten up our sluggish pace of acquisition," he said. "And we need to more vigorously recruit talented people and not scare them away. In today's world, in today's department, innovation cannot be an afterthought. It is the ballgame.''

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Ethics Key to AI Development, Austin Says > US Department of Defense > Defense Department News - Department of Defense

File photo of Googles chief executive Sundar Pichai in Brussels on Jan. 20, 2020.Photo: Virginia Mayo (AP)

The artificial intelligence revolution is poised to be more profound than the invention of electricity, the internet, and even fire, according to Google CEO Sundar Pichai, who made the comments to BBC media editor Amol Rajan in a podcast interview that first went live on Sunday.

The progress in artificial intelligence, we are still in very early stages, but I viewed it as the most profound technology that humanity will ever develop and work on, and we have to make sure we do it in a way that we can harness it to societys benefit, Pichai said.

But I expect it to play a foundational role pretty much across every aspect of our lives. You know, be it health care, be it education, be it how we manufacture things and how we consume information. And so I view it as a very profound enabling technology. You know, if you think about fire or electricity or the internet, its like that, but I think even more profound, Pichai continued.

The strange part is that Pichai never actually strictly defines artificial intelligence, a term thats often abused when people dont bother to nail down a definition.

Whether you agree with Pichai or not, its obvious that hes right about one thing: Whatever happens with AI needs to be for societys benefit. But again, Pichai never defines what hes talking about. Would the invention of the atomic bomb be viewed as something for societys benefit? The people who worked on the Manhattan Project may have been ethically conflicted about it, but they rationalized their work by recognizing what would happen if the Nazis built nuclear weapons first.

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As the interview pivoted to the national security implications of AIthe stuff dystopian science fiction is made ofPichai remained optimistic that society would use technology for good.

I definitely think there will be a competitive aspect to it. Therell be national security aspects to it. And those are all important questions. But where I draw the parallel to climate changes is profound enough that youre not going to reach safety on a unilateral basis because the world is connected, Pichai said.

And and so for you to truly solve for, you know, peaceful coexistence with AI, you would again need over time global frameworks and constructs. And everyone will get affected the same way, just like climate. And I think thats what will draw people together, Pichai continued.

Nothing is a given. We have to get there, but I do think as the world becomes more prosperous, when there is economic growth, everyone wants the same thing at the end, Pichai said. To some extent, you know, people want to do well, they want peace. And so, you know, you build on those ideals and connect places together.

The entirety of human history would likely disagree with Pichai, but who knows? Maybe human civilization will change for the better in the 2020s and robots will conduct our wars while leaving humans alone, as they imagined in the 1930s. The idea was that youd let two sides battle it out with nothing but robots, and whoever won the war with the most robots standing at the end was declared the winner. It was idealistic but surprisingly common in the interwar period after World War II and before World War II.

Amazingly, its not the first time Pichai has compared the coming AI revolution to the most important inventions in the history of humanity. Pichai made similar comments in February of 2018.

And while we dont have fully autonomous robot tanks stalking city streets and killing dissidents, were not far off technologically. So while know one knows if Pinchai is correct or not, were hoping hes correct. If Pichai is wrong, weve got a world of pain in front of us.

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Google CEO Still Insists AI Revolution Bigger Than Invention of Fire - Gizmodo

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The last 15 years have brought what Stanford University professor Erik Brynjolfsson calls the productivity paradox. While theres been continuing advances in technology, such as artificial intelligence, automation, and teleconferencing tools, the U.S. and other countries have seen flagging productivity.

But a productivity boom is coming soon, Brynjolfsson said at the recent EmTech Next conference hosted by MIT Technology Review. He pointed to advances in technology, particularly artificial intelligence programs that are as good as or better than humans at some things. Businesses should now focus on incorporating the technology into work processes and preparing employees, he said, and policymakers should make sure its adoption doesnt contribute to inequality.

Brynjolfsson has been tracking the lag between introduction of artificial intelligence and corresponding productivity gains. United States productivity grew by about 1.3% in the past decade, he said, compared to more than 2.8% in the late 1990s and early 2000s. This productivity slowdown extends to other countries as well, according to research from the Organization for Economic Cooperation and Development. Brynjolfsson predicted a productivity J-curve, in which productivity declines after a technology is introduced and then rises when businesses have been able to integrate technologies into their workflow, a trajectory over time that has a J-shape.

I think were near the bottom of that J-curve right now and were about to see the takeoff, Brynjolfsson said.

Lagging productivity can be explained two main ways, Brynjolfsson said.

Mismeasurement. Productivity is traditionally measured using a countrys gross domestic product, which is based on things that are bought and sold. But many digital goods teleconferencing, smartphone apps, Wikipedia are available for free. Even though people get some benefit from these goods, they dont show up in productivity statistics. The information sectors share of the economy has barely budged since the 1980s, Brynjolfsson noted. I think most of us realize thats just not a real representation of whats going on, he said.

Happiness surveys also fail to capture the complete picture. Brynjolfsson suggested a new metric called GDP-B that would measure the benefit people gain from items. I think its far from perfect, but its a lot more precise than happiness, and I think its a lot more meaningful than GDP, he said.

Implementation and restructuring in businesses. It isnt enough to just add new technology to an organization. Companies need a complete paradigm shift. To get the full benefit, leaders need to rethink business processes, management practices, and employee skills, Brynjolfsson said.

This intangible organizational capital is essential for companies to see benefit from technological advances, but many companies put misplaced focus on technology itself.

The complete reconceptualization of a business process takes a lot. More creativity, effort, and frankly, time, than simply plugging in new technologies into old business processes, he said. We just havent been doing that in most industries.

About a decade ago, machine learning programs had about 70% accuracy, Brynjolfsson said. They have improved rapidly, to the point that they are now better than humans at identifying some things. This makes it more likely that organizations will move to integrate this technology into their business practices as entrepreneurs and managers gravitate toward these often cheaper and more efficient approaches.

We dont need any additional advances in technology to be able to have enormous effects on productivity and wages, he said.What we do need is some significant changes in business processes. We need to rethink the way work gets done.

There are signs more businesses are taking advantage of artificial intelligence programs. The 2021 AI Index report, which Brynjolfsson co-authored, found increases in not just the quality of artificial intelligence, but also business investment in the technology. The biggest increase was in the field of drug discovery and other biological uses of AI, with a 4.5% increase in investment in drug discovery in the last year.

Powerful technology is available, and every organization has an opportunity to benefit from it, he said. Successful firms will be prepared with the skills needed in the future, and leaders should focus on reskilling their workforce.

Replacing labor with capital and human work with technology brings concerns about decreased wages and increased inequality. Brynjolfssons research has documented how machine learning affects different skills and occupations, and found that there isnt one occupation where machine learning could do all the different tasks. While machine learning will likely reorganize work, it wont mean the end of work or entire occupations, he said.

But the effects will likely be uneven. The economic pie could get bigger, but that doesnt mean everyones going to benefit, Brynjolfsson said. Theres been some evidence of this happening, he said, with his research also indicating machine learning is more likely to affect low-wage occupations.

Inequality isnt inevitable, though. Brynjolfsson argued that to a large extent, it is the result of tax and education policies. He suggested three measures that companies, institutions, and policymakers can take to make sure all workers benefit from the productivity boom:

Reskilling the workforce. Taking advantage of AI and other technologies require different sets of skills. Im not just talking about more machine learning experts. Im talking about people who do more creative work, Brynjolfsson said. And while machines are able to do rote, repetitive work, companies will need people who are skilled at interpersonal, emotional connections.

Adjusting tax policy. Capital is taxed at a lower rate than labor, which might push companies to favor technology over workers. Brynjolfsson suggested leveling the playing field, or introducing measures such as earned income tax credits that help subsidize work.

Focusing on technologies that augment workers instead of replace them. Brynjolfsson said he is working on research that shows how technologists are focused on creating programs that replicate human skills. While that may be a fun goal, it actually isnt a particularly good one in terms of helping reduce inequality. It tends to drive down wages, he said. Id rather have them focused on augmenting human labor.

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How to prepare for the AI productivity boom - MIT Sloan News

Smart Career Moves In AI

Businesses are building AI capabilities into their products, everything from automobiles to consumer electronics. Health-care organizations are using AI to deliver better services to patients while manufacturers are adding AI to their operational technology to improve efficiency. And its hard to find an IT vendor that isnt using AI in some way to make its technology smarter.

Artificial intelligence has been a hot technology in recent years and thats spurred demand for engineers and software developers who can design and develop AI and machine learning algorithms and code and build them into everything from sophisticated IT systems to everyday consumer products.

[RELATED: Artificial Intelligence Week 2021]

Job search and employment website Indeed recently took a look at AI job postings to see which AI-related jobs are the most in demand and are paying the highest median salaries in the U.S. Heres what it found.

Link:

The 10 Most In-Demand AI Jobs And Their Salaries: Indeed - CRN

Biological image processing and analysis can often be laborious and complex tasks for researchers. Aivia aims to help researchers tackle the most challenging imaging applications using artificial intelligence (AI)-guided image analysis and visualization solutions.Technology Networks spoke with Dr. Luciano Lucas, director at Leica Aivia, to learn more about the challenges of biological image analysis and how AI can help to overcome them. In this interview, Dr. Lucas also explains some of the potential barriers to wider adoption of AI in laboratories and shares his views on where AI microscopy may be headed in the future.

Anna MacDonald (AM): What challenges do researchers face when undertaking biological image analysis? How can AI help to address these issues?Dr. Luciano Lucas (LL): Researchers in the biopharma/life sciences spaces are faced with a wide range of problems when it comes to image processing and image analysis. The key issues we have identified and are working on) are:1) Development, implementation and accessibility to state-of-the-art AI technology (AI microscopy). This type of technology enables the completion of previously impossible to run experiments. However, AI microscopy is a new discipline which requires further research, validation and characterization. We and others in the community are very active on this important task. After four and a half years of R&D we feel confident enough to release to the public some of the work we have done as either pre-trained deep learning models (see our 3D RCAN paper and Aivia DL Model Library) or enabling software tools that allows everyone to leverage some key AI microscopy technology (e.g. AiviaCloud).

2) Inherent image quality. Image acquisition and image analysis are decoupled from each other in time. This often results in the creation of large amounts of image data that are not good enough for analysis.

3) Data size. This poses all sorts of issues on both the visualization and analysis fronts.

4) Result accuracy and reproducibility. Both are an essential part of the scientific discovery process.

5) Tool complexity. Making tools that are easy to learn and use is essential to adoption this is often under appreciated.

Our research work is primarily focused on item one above, but we also have active internal R&D projects to address the rest. As we address the key topics mentioned above we strive to improve the rate of scientific discovery based on image data. We believe this can be achieved by improving how we (humans) interact with software and hardware. Present day tools ignore the fact that researchers are experts in biology (or similar disciplines) and may have very limited expertise in microscopy, image analysis and/or data science/machine learning (ML)/deep learning (DL)/AI. By creating tools that acknowledge and leverage the biologists expertise we can create intelligent tools that learn (about biology) from the user. Such tools would gradually learn what a cell is and what it can look like in multiple scenarios. Ultimately, the software/hardware should be able to autonomously do the imaging and image analysis, thus allowing the researcher to focus on the creative and critical thinking portion of the scientific discovery process.

AM: How easy is it for laboratories to adopt AI? Are there any barriers that need to be overcome?LL: From the point of view of availability, it is easy. Aivia is a key example of a professionally developed and supported software platform that can be used by anyone. There are several open source projects that offer powerful technical solutions in this space too. The issue/problem for wider adoption is tool and technology complexity. AI is a new topic within the microscopy/biomedical sciences community. Thus, there are very few experts and fewer good tools.In the last three years we have seen a major increase in pre-prints and peer-reviewed publications using AI for microscopy as well as the creation of several high-profile courses and symposia on the topic (see the AI Microscopy Symposium). I expect the number of publications to continue to increase in the coming years as this type of approach splits out of the labs/groups/companies that have been pioneering it and become mainstream. The leaders in this community will need to continue their outreach and educational activities in turn this will help solve the key issues mentioned above.

It is key to create tools (software and hardware) that clearly show the value of AI for microscopy. Todays best AI-powered tools can achieve a lot in the hands of ML/DL experts but, for the most part, are not easy to use for non-experts in this space. Our team is very aware of this and is focused on creating tools (Aivia/AiviaWeb/AiviaCloud) that remove the complexity while delivering the full power of AI for microscopy applications.

AM: Can you tell us more about Aivia and what sets it apart?LL: Aivia makes AI microscopy accessible to all. From image restoration (and super resolution) to image segmentation and virtual staining, we can do it all in one easy-to-use platform. Aivia is also great for large (multi TB) data sets and has several good solutions for automation and reproducibility.

AM: What do you see in store for the future of AI in microscopy?LL: It is a true pleasure to work in this field nearly every day one comes across new ideas with significant potential to be transformative. Below are some of my favorites (not all of them used in the microscopy world at least not yet).

GPT3

Transformers

Flood filling networks

Smart microscopy

U-net

CARE

Neuromorphic processing units

Optical deep learning

Virtual staining

In the next few decades, we will gradually move from AI solutions/tools that are good (i.e. human level performance) at System 1 learning and thinking, to AI agents that can do System 2 learning and thinking. This is the true challenge both in microscopy and more generally. Humans will likely remain far superior to AI agents for tasks that require the integration and consideration of multiple, incomplete, multi-domain data sources. As we create better AI agents that can act more often in a System 2 way, humans will be able to dedicate more of their time to creative and innovative tasks, e.g. creating scientific hypotheses, designing experiments to test those and interpreting the insights provided by the AI agents.

Dr. Luciano Lucas was speaking to Anna MacDonald, science writer for Technology Networks.

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AI in Microscopy: Opportunities, Challenges and the Future - Technology Networks