AIDaily Roundup starts today! We are covering the top updates from around the world. The updates will feature state-of-the-art capabilities inartificial intelligence,Machine Learning,Robotic Process Automation,Fintechand human-system interactions. We will cover the role of AI Daily Roundup and their application in various industries and daily lives.

Sprinklr Joins Adobe Exchange Partner Program To Help Marketers Manage Paid, Owned And Earned Campaigns Across Modern Channels

Sprinklr, the Customer Experience Management (CXM) platform for modern enterprises, is now a partner in the Adobe Exchange partner program, which recognizes a select group of innovative solutions that are critical to the success ofAdobecustomers.

Atos and RingCentral Launch Unify Office by RingCentral in Germany

Atos SE, a global leader in digital transformation, andRingCentral, Inc, a leading provider of global enterprisecloud communications, collaboration, and contact center solutions, announced the launch of Unify Office by RingCentral in Germany.

HP Introduces New Era of Virtual Reality for Developers and Enterprises

HP Inc.unveiled theHPOmniceptSolution, bringing the worlds most intelligent VR headset and a developer focused SDK into a single platform, equipping VR software developers with an ecosystem to create new hyper-personalized, engaging, and adaptive VR experiences for enterprises.

Accenture Collaborates With Oracle to Transform Nickel Banks Finance Functions

Accenture,a Platinum level member ofOracle PartnerNetwork(OPN) completed an information technology (IT) modernization project for Nickel, a subsidiary of BNP Paribas and the first French neo-bank, that transformed Nickels finance functions with the implementation of Oracle software-as-a-service (SaaS) andEnterprise Resource Planning(ERP) solutions.

Gartner Analyst Gorka Sadowski Joins Exabeam as Chief Strategy Officer

Exabeam, the Smarter SIEM company, announced the appointment of industry veteran and former Gartner analystGorka Sadowskito chief strategy officer. Exabeam has grown rapidly over the past six years as it has executed on its vision for enhancingsecurityteams with analytics and automation.

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Daily AI Roundup: The 5 Coolest Things On Earth Today - AiThority

Siemens USA, a manufacturer of industrial and health-care equipment, uses AIOps through its endpoint detection and response system that incorporates machine learning, the subset of AI that enables systems to learn and improve. The system gathers data from endpointshardware devices such as laptops and PCsand then analyzes the data to reveal potential threats. The organizations overall cybersecurity approach also uses data analytics, which allows it to quickly and efficiently parse through numerous log sources. The technology provides our security analysts with actionable outputs and enables us to remain current with threats and indicators of compromise, Mahmood says.

AIOps is a broad category of tools and components that uses AI and analytics to automate common IT operational processes, detect and resolve problems, and prevent costly outages. Machine-learning algorithms monitor across systems, learning as they go how systems perform, and detect problems and anomalies. Now, as adoption of AIOps platforms gains momentum, industry observers say IT decision-makers will increasingly use the technology to bolster cybersecuritylike Siemens, in integration with other security toolsand guard against a multitude of threats. This is happening against a backdrop of mounting complexity in organizations application environments, spanning public and private cloud deployments, and their perennial need to scale up or down in response to business demand. Further, the massive migration of employees to their home offices in an effort to curb the deadly pandemic amounts to an exponential increase in the number of edge-computing devices, all which require protection.

A May report from Global Industry Analysts predicts the AIOps platform market worldwide will grow by an estimated $18 billion this year, driven by a compounded growth rate of 37%.1 It also projects that AIOps initiativesparticularly among big corporationswill span the entire corporate ecosystem, from on-premises to public, private, and hybrid clouds to the network edge, where resources and IT staff are scarce. Most recently, a well-documented rise in data breaches, particularly during the pandemic, has underscored the need to deliver strong, embedded security with AIOps platforms.

Cybersecurity affects every aspect of business and IT operations. The sheer number of near-daily breaches makes it difficultif not impossiblefor organizations, IT departments, and security professionals to cope. In the last year, 43% of companies worldwide reported multiple successful or attempted data breaches, according to an October 2019 survey conducted by KnowBe4, a security awareness training company.2 Nearly two-thirds of respondents worry their organizations may fall victim to a targeted attack in the next 12 months, and today concern is further fueled by the growing number of cybercrimes amid disarray caused by the pandemic. Organizations need to use every technological means at their disposal to thwart hackers.

The strongest AIOps platforms can help organizations proactively identify, isolate, and respond to security issues, helping teams assess the relative impact on the business. They can determine, for example, whether a potential problem is ransomware, which infiltrates computer systems and shuts down access to critical data. Or they can ferret out threats with longer-term effects, such as leaking customer data and in turn causing massive reputational damage. Thats because AIOps platforms have full visibility into an organizations data, spanning traditional departmental silos. They apply analytics and AI to the data to determine the typical behavior of an organizations systems. Once they have that baseline state, the platforms do continual reassessments of the networkand all wired and wireless devices communicating on itand zero in on outlier signals. If theyre suspiciousexceeding a threshold defined by AIan alert is sent to IT security staffers detailing the threat, the degree to which it could disrupt the business, and the steps they need to take to eliminate it.

Download the full report.

Read more from the original source:

AIOps uses AI, automation to boost security - MIT Technology Review

After releasing what may well have been the most comprehensive report on the State of AI in 2019, Air Street Capital and RAAIS founder Nathan Benaich and AI angel investor, and UCL IIPP Visiting Professor Ian Hogarth are back for more.

In the State of AI Report 2020 released today, Benaich and Hogarth outdid themselves. While the structure and themes of the report remain mostly intact, its size has grown by nearly 30 percent. This is a lot, especially considering their 2019 AI report was already a 136 slide long journey on all things AI.

The State of AI Report 2020 is 177 slides long, and it covers technology breakthroughs and their capabilities, supply, demand and concentration of talent working in the field, large platforms, financing and areas of application for AI-driven innovation today and tomorrow, special sections on the politics of AI, and predictions for AI.

ZDNet caught up with Benaich and Hogarth to discuss their findings.

We set out by discussing the rationale for such a substantial contribution, which Benaich and Hogarth admitted to having taken up an extensive amount of their time. They mentioned their feeling is that their combined industry, research, investment and policy background and currently held positions give them a unique vantage point. Producing this report is their way of connecting the dots and giving something of value back to the AI ecosystem at large.

Coincidentally, Gartner's 2020 Hype cycle for AI was also released a couple of days back. Gartner identifies what it calls 2 megatrends that dominate the AI landscape in 2020 -- democratization and industrialization. Some of Benaich and Hogarth's findings were about the massive cost of training AI models, and the limited availability of research. This seems to contradict Gartner's position, or at least imply a different definition of democratization.

Benaich noted that there are different ways to look at democratization. One of them is the degree to which AI research is open and reproducible. As the duo's findings show, it is not: only 15% of AI research papers publish their code, and that has not changed much since 2016.

Hogarth added that traditionally AI as an academic field has had an open ethos, but the ongoing industry adoption is changing that. Companies are recruiting more and more researchers (another theme the report covers), and there is a clash of cultures going on as companies want to retain their IP. Notable organizations criticized for not publishing code include OpenAI and DeepMind:

"There's only so close you can get without a sort of major backlash. But at the same time, I think that data clearly indicates that they're certainly finding ways to be close when it's convenient", said Hogarth.

Industrialization of AI is under way, as open source MLOps tools help bring models to production

As far as industrialization goes, Benaich and Hogarth pointed towards their findings in terms of MLOps. MLOps, short for machine learning operations, is the equivalent of DevOps for ML models: taking them from development to production, and managing their lifecycle in terms of improvements, fixes, redeployments and so on.

Some of the more popular and fastest growing Github projects in 2020 are related to MLOps, the duo pointed out. Hogarth also added that for start up founders, for example, it's probably easier to get started with AI today than it was a few years ago, in terms of tool availability and infrastructure maturity. But there is a difference when it comes to training models like GPT3:

"If you wanted to start a sort of AGI research company today, the bar is probably higher in terms of the compute requirements. Particularly if you believe in the scale hypothesis, the idea of taking approaches like GPT3 and continuing to scale them up. That's going to be more and more expensive and less and less accessible to new entrants without large amounts of capital.

The other thing that organizations with very large amounts of capital can do is run lots of experiments and iterates in large experiments without having to worry too much about the cost of training. So there's a degree to which you can be more experimental with these large models if you have more capital.

Obviously, that slightly biases you towards these almost brute force approaches of just applying more scale, capital and data to the problem. But I think that if you buy the scaling hypothesis, then that's a fertile area of progress that shouldn't be dismissed just because it doesn't have deep intellectual insights at the heart of it".

This is another key finding of the report: huge models, large companies and massive training costs dominate the hottest area of AI today: NLP - Natural Language Processing. Based on variables released by Google et. al., research has estimated the cost of training NLP models at about $1 per 1000 parameters.

That means that a model such as OpenAI's GPT3, which has been hailed as the latest and greatest achievement in AI, could have cost tens of millions to train. Experts suggest the likely budget was $10M. That clearly shows that not everyone can aspire to produce something like GPT3. The question is, is there another way? Benaich and Hogarth think so, and have an example to showcase.

PolyAI is a London-based company active in voice assistants. They produced, and open sourced, a conversational AI model (technically, a pre-trained contextual re-ranker based on transformers) that outperforms Google's BERT model in conversational applications. PolyAI's model not only performs much better than Google's, but it required a fraction of the parameters to train, meaning also a fraction of the cost.

PolyAI managed to produce a machine learning language models that performs better than Google in a specific domain, at a fraction of the complexity and cost.

The obvious question is, how did PolyAI did it, as this could be inspiration for others, too. Benaich noted that the task of detecting intent and understanding what somebody on the phone is trying to accomplish by calling is solved in a much better way by treating this problem as what is called a contextual re-ranking problem:

"That is, given a kind of menu of potential options that a caller is trying to possibly accomplish based on our understanding of that domain, we can design a more appropriate model that can better learn customer intent from data than just trying to take a general purpose model -- in this case BERT.

BERT can do OK in various conversational applications, but just doesn't have kind of engineering guardrails or engineering nuances that can make it robust in a real world domain. To get models to work in production, you actually have to do more engineering than you have to do research. And almost by definition, engineering is not interesting to the majority of researchers".

Long story short: you know your domain better than anyone else. If you can document and make use of this knowledge, and have the engineering rigor required, you can do more with less. This once more pointed to the topic of using domain knowledge in AI. This is what critics of the brute force approach, also known as the "scaling hypothesis", point to.

What the proponents of the scaling hypothesis seem to think, simplistically put, is that intelligence is an emergent phenomenon relating to scale. Therefore, by extension, if at some point models like GPT3 become large enough, complex enough, the holy grail of AI, and perhaps science and engineering at large, artificial general intelligence (AGI), can be achieved.

How to make progress in AI, and the topic of AGI, is at least as much about philosophy as it is about science and engineering. Benaich and Hogarth approach it in a holistic way, prompted by the critique to models such as GPT3. The most prominent critic to approaches such as GPT3 is Gary Marcus. Marcus has been consistent in his critique of models predating GPT3, as the "brute force" approach does not seem to change regardless of scale.

Benaich referred to Marcus' critique, summing it up. GPT3 is an amazing language model that can take a prompt and output a sequence of text that is legible and comprehensible and in many cases relevant to what the prompt was. What's more, we should add, GPT3 can even be applied to other domains, such as writing software code for example, which is a topic in and of its own.

However, there are numerous examples where GPT3 is off course, either in a way that expresses bias, or it just produces, irrelevant results. An interesting point is how we are able to measure the performance of models like GPT3. Benaich and Hogarth note in their report that existing benchmarks for NLP, such as GLUE and SuperGLUE are now being aced by language models.

These benchmarks are meant to compare the performance of AI language models against humans at a range of tasks spanning logic, common sense understanding, and lexical semantics. A year ago, the human baseline in GLUE was beat by 1 point. Today, GLUE is reliably beat, and its more challenging sibling SuperGLUE is almost beat too.

AI language models are getting better, but does that mean we are approaching artificial general intelligence?

This can be interpreted in a number of ways. One way would be to say that AI language models are just as good as humans now. However, the kind of deficiencies that Marcus points out show this is not the case. Maybe then what this means is that we need a new benchmark. Researchers from Berkeley have published a new benchmark, which tries to capture some of these issues across various tasks.

Benaich noted that an interesting extension towards what GPT3 could do relates to the discussion around PolyAI. It's the aspect of injecting some kind of toggles to the model that allow it to have some guardrails, or at least tune what kind of outputs it can create from a given input. There are different ways that you might be able to do this, he went on to add.

Previously, the use of knowledge bases and knowledge graphs was discussed. Benaich also mentioned some kind of learned intent variable that could be used to inject this kind of control over this more general purpose sequence generator. Benaich thinks the critical view is certainly valid to some degree, and points to what models like GPT3 could use, with the goal of making them useful in production environments.

Hogarth on his part noted that Marcus is "almost a professional critic of organizations like DeepMind and OpenAI". While it's very healthy to have those critical perspectives when there is reckless hype cycle around some of this work, he went on to add, OpenAI has one of the more thoughtful approaches to policy around this.

Hogarth emphasized the underlying difference in philosophy between proponents and critics of the scaling hypothesis. However, he went on to add, if the critics are wrong, then we might have a very smart but not very well-adjusted AGI on our hands as as evidenced by sort of some of these early instances of bias as you scale these models:

"So I think it's incumbent on organizations like OpenAI if they are going to pursue this approach to tell us all how they're going to do it safely, because it's not obvious yet from their research agenda. How do you marry AI safety with this kind of this kind of throw more data and compute to the problem and AGI will emerge approach".

This discussion touched on another part of the State of AI Report 2020. Some researchers, Benaich and Hogarth noted, feel that progress in mature areas of machine learning is stagnant. Others call for a advancing causal reasoning, and claim that adding this element to machine learning approaches could overcome barriers.

Adding causality to machine learning could be the next breakthrough. The work of pioneers like Judea Pearl shows the way

Causality, Hogarth said, is arguably at the heart of much of human progress. From an epistemological perspective, causal reasoning has given us the scientific method, and it's at the heart of all of our best world models. So the work that people like Judea Pearl have pioneered to bring causality to machine learning is exciting. It feels like the biggest potential disruption to the general trend of larger and larger correlation driven models:

"I think if you can if you can crack causality, you can start to build a pretty powerful scaffolding of knowledge upon knowledge and have machines start to really contribute to our own knowledge bases and scientific processes. So I think it's very exciting. There's a reason that some of the smartest people in machine learning are spending weekends and evenings working on it.

But I think it's still in its infancy as an area of attention for the commercial community. We really only found one or two examples of it being used in the wild, one by faculty at a London based machine learning company and one by BenevolentAI in our report this year".

If you thought that's enough cutting edge AI research and applications for one report, you'd be wrong. The State of AI Report 2020 is a trove of references, and we'll revisit it soon, with more insights from Benaich and Hogarth.

More here:

The state of AI in 2020: democratization, industrialization, and the way to artificial general intelligence - ZDNet

When applied to images of x-rays to detect pneumonia, errors by radiologists were rare when the x-ray images had clear features. However, UDC found the diagnosis (or label) for several x-ray images to be neither correct nor an error. Verification of these images by an independent radiologist also agreed that they were indeed difficult images to diagnose, with their independent assessment often disagreeing with the original diagnosis provided in the public dataset. Similarly, AI that was trained to diagnose pneumonia also found the assessment difficult for these images.

Removal of poor-quality (difficult) images identified by UDC from the training dataset improved AI accuracy for diagnosing pneumonia in x-rays images by over 10% as measured on a hold out blind test set, and the AI was shown to be more scalable (generalizable). The accuracy also exceeded benchmarks set by the current literature for that public dataset.

The AI Scientist that led the project, Dr Milad Dakka, said "Our results suggest these poor-quality images are uninformative, counter-productive or confusing when used in training AI. The ability to identify when new images are poor-quality is important to prevent an inaccurate AI clinical assessment, but also to alert the radiologist when the scan is likely to be difficult to diagnose or when a new scan should be taken."

Presagen Co-Founder and Chief Strategy Officer, Dr Don Perugini said "Many AI practitioners believe that AI performance and scalability can be solved with more data. This is not true, and we call it the AI data fallacy. Even 1% poor-quality data can impact the performance of the AI. Building accurate and scalable AI is about using the right data."

Presagen has recently developed a range of patent-pending AI technologies that drive a fundamental paradigm shift in developing commercially scalable AI products for real-world problems, which apply beyond healthcare and to AI more generally.

Dr Michelle Perugini said "We are excited to present to the world the suite of technologies, which we believe advance the field of AI. These technologies will allow Presagen to build scalable 'out of the box' AI products that are more commercially viable and technically superior, and thus market dominating. This is vital in Presagen's journey to become world-leaders in AI Enhanced Healthcare and a dominant player in the AI-in-Femtech market globally. More importantly, we see it as an opportunity to change, lead, and dominate the AI industry."

SOURCE Presagen

https://www.presagen.com/

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Turning AI onto itself: AI algorithm detects when medical images will be difficult for radiologists or AI to make an effective diagnosis - PRNewswire

"A lot of people in academia are not very good at software engineering," says Kenny Daniel, co-founder and chief technology officer of cloud computing startup Algorithmia. "I always had more of the software engineering bent."

That, in a nutshell, is some of what makes six-year-old, Seattle-based Algorithmia uniquely focused in a world over-run with machine learning offerings.

Amazon, Microsoft, Google, IBM, Salesforce, and other large companies have for some time been offering cut-and-paste machine learning in their cloud services. Why would you want to stray to a small, young company?

No reason, unless that startup had a particular knack for hands-on support of machine learning.

That's the premise of Daniel's firm, founded with Diego Oppenheimer, a graduate of Carnegie Mellon and a veteran of Microsoft. The two became best friends in undergrad at CMU, and when Oppenheimer went to industry, Daniel went to pursue a PhD in machine learning at USC. While researching ML, Daniel realized he wanted to build things more than he wanted to just theorize.

"I had the idea for Algorithmia in grad school," Daniel recalled in an interview with ZDNet. "I saw the struggle of getting the work out into the real world; my colleagues and I were developing state-of-the-art [machine learning] models, but not really getting them adopted in the real world the way we wanted."

He dropped out of USC and hooked up with Oppenheimer to found the company. Oppenheimer had seen from the industry side that even for large companies such as Microsoft, there was a struggle to get enough talent to get things deployed and in production.

The duo initially set out to create an App Store for machine learning, a marketplace in which people could buy and sell ML models, or programs. They got seed funding from venture firm Madrona Ventures, and took up residence in Seattle's Pike Place. "There's a tremendous amount of ML talent out here, and the rents are not as crazy" as Silicon Valley, he explained.

"If companies are not getting the pay-off, if there's a lack of progress, we could be looking at another hype cycle," says Kenny Daniel, CTO and co-founder of machine learning operations service provider Algorithmia.

Their intent was to match up consumers of machine learning, companies that wanted the models, with developers. But Daniel noticed something was breaking down. The majority of customers using the service were consuming machine learning from their own teams. There was little transaction volume because companies were just trying to get stuff to work.

"We said, okay, there's something else going on here: people don't have a great way of turning their models into scalable, production-ready APIs that are highly available and resilient," he recalled having realized.

"A lot of these companies would have data scientists building models in Jupyter on their laptop, and not really having a good way to hook them up to a million iOS apps that are trying to recognize images, or a back-end data pipeline that's trying to process terabytes of data a day."

There was, in other words, "a gap there in software engineering." And so the business shifted from a focus on a marketplace to a focus on providing the infrastructure to make customers' machine learning models scale up.

The company had to solve a lot of the multi-tenant challenges that were fundamental limitations, long before those techniques became mainstream with the big cloud platforms.

Also: How do we know AI is ready to be in the wild? Maybe a critic is needed

"We were running functions before AWS Lambda," says Daniel, referring to Amazon's server-less offering.

Problems such as, "How do you manage GPUs, because GPUs were not built for this kind of thing, they were built to make games run fast, not for multi-tenant users to run jobs on them."

Daniel and Oppenheimer started meeting with big financial and insurance firms, to discuss solving their deployment problems. Training a machine learning model might be fine on AWS. But when it came time to make predictions with the trained model, to put it into production for a high volume of requests, companies were running into issues.

The companies wanted their own instances of their machine learning models in virtual private clouds, on AWS or Azure, with the ability to have dedicated customer support, metrics, management and monitoring.

That lead to the creation of an Algorithmia Enterprise service in 2016. That was made possible by fresh capital, an infusion of $10.5 million from Gradient Ventures, Google's AI investment operation, followed by a $25 million round last summer. In total. Algorithmia has received $37.9 million in funding.

Today, the company has seven-figure deals with large institutions, most of it for running private deployments. You could get something like what Algorithmia offers by using Amazon's SageMaker, for example. But SageMaker is all about using only Amazon's resources. The appeal with Algorithmia is that the deployments will run in multiple cloud facilities, wherever a customer needs machine learning to live.

"A number of these institutions need to have parity across wherever their data is," said Daniel. "You may have data on premise, or maybe you did acquisitions, and things are across multiple clouds; being able to have parity across those is one of the reasons people choose Algorithmia."

Amazon and other cloud giants each tout their offerings as end-to-end services, said Daniel. But that runs counter to reality, which is that there is a soup composed of many technologies that need to be brought together to make ML work.

"In the history of software, there hasn't been a clear end-to-end, be-all winner," Daniel observed. "That's why GitHub, and GitLab, and Bitbucket and all these continue to exist, and there are different CI [continuous integration] systems, and Jenkins, and different deployment systems and different container systems."

"It takes a fair amount of expertise to wire all these things together."

There is some independent support for what Daniel claims. Gartner analyst Arun Chandrasekaran puts Algorithmia in a basket that he calls "ModelOps." The application "life cycle" of artificial intelligence programs,

Chandrasekaran told ZDNet, is different from that of traditional applications, "due to the sheer complexity and dynamism of the environment."

"Most organizations underestimate how long it will take to move AI and ML projects into production."

Also: Recipe for selling software in a pandemic: Be essential, add some machine learning, and focus, focus, focus

Chandrasekaran predicts the market for ModelOps will expand as more and more companies try to deploy AI and run up against the practical hurdles.

While there is the risk that cloud operators will subsume some of what Algorithmia offers, said Chandrasekaran, the need to deploy outside a single cloud supports the role of independent ModelOps vendors such as Algorithmia.

"AI deployments tend to be hybrid, both from the perspective of spanning multiple environments (on-premises, cloud) as well as the different AI techniques that customers may use," he told ZDNet.

Aside from cloud vendors, Algorithmia competitors include Datarobot, H20.ai, RapidMiner, Hydrosphere, Modelop and Seldon.

Some companies may go 100% AWS, conceded Daniel. And some customers may be fine with generic abilities of cloud vendors. For example, Amazon has made a lot of progress with text translation technology as a service, he noted.

But industry-specific, or vertical market machine learning, is something of a different story. One customer of Algorithmia, a large financial firm, needed to deploy an application for fraud detection. "It sounds crazy, but we had to figure out all this stuff of, how do we know this data over here is used to train this model? It's important because its an issue of their [the client's] liability."

The immediate priority for Algorithmia is a new product version called Teams that lets companies organize an invite-only, hosted gathering of those working on a particular model. It can stretch across multiple "federated" instances of a model, said Daniel. The pricing is by compute usage, so it's a pay-as-you-go option, versus the annual billing of the Enterprise version.

Also: AI startup Abacus goes live with commercial deep learning service, takes $13M Series A financing

To Daniel, the gulf that he observed in academia between pure research and software engineering is the thing that has always shot down AI in past. The so-called "AI winter" periods over the decades were in large part a result of the practical obstacles, he believes.

"Those were periods when there was hype for AI and ML, and companies invested a lot of money," he said. "If companies are not getting the pay-off, if there's a lack of progress, we could be looking at another hype cycle."

By contrast, if more companies can be successful in deployment, it may lead to a flourishing of the kind of marketplace that he and Oppenheimer originally envisioned.

"It's like the Unix philosophy, these small things combining, that's the way that I see it," he said. "Ultimately, this will just enable all sorts of things, completely new scenarios, and that's incredibly valuable, things that we can make available in a free market of machine learning."

Read more here:

Will AI cross the proverbial chasm? Algorithmia resolves the practical pitfalls of machine learning - ZDNet

Ask Stefan Jockusch what a factory might look like in 10 or 20 years, and the answer might leave you at a crossroads between fascination and bewilderment. Jockusch is vice president for strategy at Siemens Digital Industries Software, which develops applications that simulate the conception, design, and manufacture of products like cell phones or smart watches. His vision of a smart factory is abuzz with independent, moving robots. But they dont stop at making one or three or five things. Nothis factory is self-organizing.

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.

Depending on what product I throw at this factory, it will completely reshuffle itself and work differently when I come in with a very different product, Jockusch says. It will self-organize itself to do something different.

Behind this factory of the future is artificial intelligence (AI), Jockusch says in this episode of Business Lab. But AI starts much, much smaller, with the chip. Take automaking. The chips that power the various applications in cars todayand the driverless vehicles of tomorroware embedded with AI, which support real-time decision-making. Theyre highly specialized, built with specific tasks in mind. The people who design chips then need to see the big picture.

You have to have an idea if the chip, for example, controls the interpretation of things that the cameras see for autonomous driving. You have to have an idea of how many images that chip has to process or how many things are moving on those images, Jockusch says. You have to understand a lot about what will happen in the end.

This complex way of building, delivering, and connecting products and systems is what Siemens describes as chip to citythe idea that future population centers will be powered by the transmission of data. Factories and cities that monitor and manage themselves, Jockusch says, rely on continuous improvement: AI executes an action, learns from the results, and then tweaks its subsequent actions to achieve a better result. Today, most AI is helping humans make better decisions.

We have one application where the program watches the user and tries to predict the command the user is going to use next, Jockusch says. The longer the application can watch the user, the more accurate it will be.

Applying AI to manufacturing can result in cost savings and big gains in efficiency. Jockusch gives an example from a Siemens factory of printed circuit boards, which are used in most electronic products. The milling machine used there has a tendency to goo up over timeto get dirty. The challenge is to determine when the machine has to be cleaned so it doesnt fail in the middle of a shift.

We are using actually an AI application on an edge device that's sitting right in the factory to monitor that machine and make a fairly accurate prediction when it's time to do the maintenance, Jockusch says.

The full impact of AI on businessand the full range of opportunities the technology can uncoveris still unknown.

There's a lot of work happening to understand these implications better, Jockusch says. We are just at the starting point of doing this, of really understanding what can optimization of a process do for the enterprise as a whole.

Business Lab is hosted by Laurel Ruma, 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 partnership with Siemens Digital Industries Software.

Siemens helps Vietnamese car manufacturer produce first vehicles, Automation.com, September 6, 2019

Chip to city: the future of mobility, by Stefan Jockusch, The International Society for Optics and Photonics Digital Library, September 26, 2019

Laurel Ruma: From MIT Technology Review, I'm 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 artificial intelligence and physical applications. AI can run on a chip, on an edge device, in a car, in a factory, and ultimately, AI will run a city with real-time decision-making, thanks to fast processing, small devices, and continuous learning. Two words for you: smart factory.

My guest is Dr. Stefan Jockusch, who is vice president for strategy for Siemens Digital Industries Software. He is responsible for strategic business planning and market intelligence, and Stefan also coordinates projects across business segments and with Siemens Digital Leadership. This episode of Business Lab is produced in association with Siemens Digital Industries. Welcome, Stefan.

Stefan Jockusch: Hi. Thanks for having me.

Laurel: So, if we could start off a bit, could you tell us about Siemens Digital Industries? What exactly do you do?

Stefan: Yeah, in the Siemens Digital Industries, we are the technical software business. So we develop software that supports the whole process from the initial idea of a product like a new cell phone or smartwatch, to the design, and then the manufactured product. So that includes the mechanical design, the software that runs on it, and even the chips that power that device. So with our software, you can put all this into the digital world. And we like to talk about what you get out of that, as the digital twin. So you have a digital twin of everything, the behavior, the physics, the simulation, the software, and the chip. And you can of course use that digital twin to basically do any decision or try out how the product works, how it behaves, before you even have to build it. That's in a nutshell what we do.

Laurel: So, staying on that idea of the digital twin, how do we explain the idea of chip to city? How can manufacturers actually simulate a chip, its functions, and then the product, say, as a car, as well as the environment surrounding that car?

Stefan: Yeah. Behind that idea is really the thought that in the future, and today already we have to build products, enabling the people who work on that to see the whole, rather than just a little piece. So this is why we make it as big as to say from chip to city, which really means, when you design a chip that runs in a vehicle of today and more so in the future, you have to take a lot of things into account while you are designing that chip. You have to have an idea if the chip, for example, controls the interpretation of things that the cameras see for autonomous driving, you have to have an idea how many images that chip has to process or how many things are moving on those images and obvious pedestrians, what recognition do you have to do? You have to understand a lot about what will happen in the end. So the idea is to enable a designer at the chip level to understand the actual behavior of a product.

And what's happening today, especially is that we don't develop cars anymore just with a car in mind, we more and more are connecting vehicles to the environment, to each other. And one of the big purposes, as we all know, that is of course, to improve the contamination in cities and also the traffic in cities, so really to make these metropolitan areas more livable. So that's also something that we have to take into account in this whole process chain, if we want to see the whole as a designer. So this is the background of this whole idea, chip to city. And again, the way it should look like for a designer, if you think about, I'm designing this vision module in a car, and I want to understand how powerful it has to be. I have a way to immerse myself into a simulation, a very accurate one, and I can see what data my vehicle will see, what's in them, how many sensor inputs I get from other sources, and what I have to do. I can really play through all of that.

Laurel: I really like that framing of being able to see the whole, not just the piece of this incredibly complex way of thinking, building, delivering. So to get back down to that piece level, how does AI play a role at the chip level?

Stefan: AI is a lot about supporting or even making the right decision in real time. And that's I think where AI and the chip level become so important together, because we all know that a lot of smart things can be done if you have a big computer sitting somewhere in a data center. But AI and the chip level is really very targeted at these applications that need real-time performance and a performance that doesn't have time to communicate a lot. And today it's really evolving to that the chips that do AI applications are now designed already in a very specialized way, whether they have to do a lot of compute power or whether they have to conserve energy as best as they can, so be very low power consumption or whether they need more memory. So yeah, it's becoming more and more commonplace thing that we see AI embedded in tiny little chips, and then probably in future cars, we will have a dozen or so semiconductor-level AI applications for different things.

Laurel: Well, that brings up a good point because it's the humans who are needing to make these decisions in real time with these tiny chips on devices. So how does the complexity of something like continuous learning with AI, not just help the AI become smarter but also affect the output of data, which then eventually, even though very quickly, allows the human to make better decisions in real time?

Stefan: I would say most applications of AI today are rather designed to help a human make a good decision rather than making the decision. I don't think we trust it quite that much yet. So as an example, in our own software, like so many makers of software, we are starting to use AI to make it easier and faster to use. So for example, you have these very complex design applications that can do a lot of things, and of course they have hundreds of menus. So we have one application where the program watches the user and tries to predict the command the user is going to use next. So just to offer it and just say, "Aren't you about to do this?" And of course, you talked about the continuous improvement, continuous learningthe longer the application can watch the user, the more accurate it will be.

It's currently already at a level of over 95%, but of course continuous learning improves it. And by the way, this is also a way to use AI not just to help a single user but to start encoding a knowledge, an experience, a varied experience of good users and make it available to other users. If a very experienced engineer does that and uses AI and you basically take those learned lessons from that engineer and give it to someone less experienced who has to do a similar thing, that experience will help the new user as well, the novice user.

Laurel: That's really compelling because you're rightyou're building a knowledge database, an actual database of data. And then also this all helps the AI eventually, but then also really does help the human because you are trying to extend this knowledge to as many people as possible. Now, when we think about that and AI at the edge, how does this change opportunities for the business, whether you're a manufacturer or the person using the device?

Stefan: Yeah. And in general, of course, it's a way for everyone who makes a smart product to differentiate, to create differentiation because all these, the functions enabled by AI of course are smart, and they give some differentiation. But the example I just mentioned where you can predict what a user will do, that of course is something that many pieces of software don't have yet. So it's a way to differentiate. And it certainly opens lots of opportunities to create these very highly differentiated pieces of functionality, whether it's in software or in vehicles, in any other area.

Laurel: So if we were actually to apply this perhaps to a smart factory and how people think of a manufacturing chain, first this happens, and then that happens and a car door is put on and then an engine is put in or whatever. What can we apply to that kind of traditional way of thinking of a factory and then apply this AI thinking to it?

Stefan: Well, we can start with the oldest problem a factory has had. I mean, factories have always been about producing something very efficiently and continuously and leveraging the resources. So any factory tries to be up and running whenever it's supposed to be up and running, have no unpredicted or unplanned downtime. So AI is starting to become a great tool to do this. And I can give you a very hands-on example from a Siemens factory that does printed circuit boards. And one of the steps they have to do is milling of these circuit boards. They have a milling machine and any milling machine, especially one like that that's highly automated and robotic, it has a tendency to goo up over time, to get dirty. And so one challenge is to have the right maintenance because you don't want the machine to fail right in the middle of a shift and create this unplanned downtime.

So one big challenge is to figure out when this machine has to be maintained, without of course, maintaining it every day, which would be very expensive. So we are using actually an AI application on an edge device that's sitting right in the factory, to monitor that machine and make a fairly accurate prediction when it's time to do the maintenance and clean the machine so it doesnt fail in the next shift. So this is just one example, and I believe there is hundreds of potential applications that may not be totally worked out yet in this area of really making sure that factories produce consistent high quality, that there's no unplanned downtime of the machines. There's of course, a lot of use already of AI in visual quality inspections. So there's tons and tons of applications on the factory floor.

Laurel: And this has massive implications for manufacturers, because as you mentioned, it saves money, right? So is this a tough shift, do you think, for executives to think about investing in technology in a bit of a different way to then get all of those benefits?

Stefan: Yeah. It's like with every technology, I wouldn't think it's a big block, there's a lot of interest at this point and there's many manufacturers with initiatives in that space. So I would say it's probably going to create a significant progress in productivity, but of course, it also means investment. And I can say since it's fairly predictable to see what the payback of this investment will be. As far as we can see, there's a lot of positive energy there, to make this investment and to modernize factories.

Laurel: What kind of modernizations you need for the workforce in the factories when you are installing and applying, kind of retooling to have AI applications in mind?

Stefan: That's a great question because sometimes I would say many users of artificial intelligence applications probably don't even know they're using one. So you basically get a box and it will tell you, is recommended to maintain this machine now. The operator probably will know what to do, but not necessarily know what technology they're working with. But that said of course there will probably will be some, I would say, almost emerging specialties or emerging skills for engineers to really, how to use and how to optimize these AI applications that they use on the factory floor. Because as I said, we have these applications that are up and running and working today, but to get to those applications to be really useful, to be accurate enough, that of course, to this point needs a lot of expertise, at least some iteration as well. And there's probably not too many people today who really are experienced enough with the technologies and also understand the factory environment well enough to do this.

I think this is a fairly, pretty rare skill these days and to make this a more commonplace application of course we will have to create more of these experts who are really good at making AI factory-floor-ready and getting it to the right maturity.

Laurel: That seems to be an excellent opportunity, right? For people to learn new skills. This is not an example of AI taking away jobs and that more negative connotations that you get when you talk about AI and business. In practice, if we combine all of this and talk about VinFast, the Vietnamese car manufacturer that wanted to do things quite a bit differently than traditional car manufacturing. First, they built a factory, but then they applied that kind of overarching thinking of chip to factory and then eventually to city. So coming back full circle, why is this thinking unique, especially for a car manufacturer and what kind of opportunities and challenges do they have?

Stefan: Yeah. VinFast is an interesting example because when they got into making vehicles, they basically started on a green field. And that is probably the biggest difference between VinFast and the vast majority of the major automakers. That all of them are a hundred or more years old and have of course a lot of history, which then translates into having existing factories or having a lot of things that were really built before the age of digitalization. So VinFast started from a greenfield, and that of course is a big challenge, it makes it very difficult. But the advantage was that they really have the opportunity to start off with a full digitalized approach, that they were able to use software. Because they were basically constructing everything, and they could really start off with this fairly complete digital twin of not only their product but also they designed the whole factory on a computer before even starting to build it. And then they build it in record time.

So that's probably the big, unique aspect that they have this opportunity to be completely digital. And once you are at that state, once you can already say my whole design, of course, my software running on the vehicle, but also my whole factory, my whole factory automation. I already have this in a fully digital way and I can run through simulations and scenarios. That also means you have a great starting point to use these AI technologies to optimize your factory or to help the workers with the additional optimizations and so on.

Laurel: Do you think it's impossible to be one of those hundred-year-old manufacturers and slowly adopt these kinds of technologies? You probably don't have to have a greenfield environment, it just makes everything easy or I should say easier, right?

Stefan: Yeah. All of them, I mean the auto industry has traditionally been one of the one that invested most in productivity and in digitalization. So all of them are on that path. Again, they don't have this very unique situation that you, or rarely have this unique situation that you can really start from a blank slate. But a lot of the software technology of course, also is adapted to that scenario. Where for example, you have an existing factory, so it doesn't help you a lot to design a factory on the computer if you already have one. So you use these technologies that allow you to go through the factory and do a 3D scan. So you know exactly how the factory looks like from the inside without having it designed in a computer, because you essentially produce that information after the fact. So that's definitely what the established or the traditional automakers do a lot and where they're also basically bringing the digitalization even into the existing environment.

Laurel: We're really discussing the implications when companies can use simulations and scenarios to apply AI. So when you can, whether or not it's greenfield or you're adopting it for your own factory, what happens to the business? What are the outcomes? Where are some of the opportunities that are possible when AI can be applied to the actual chip, to the car, and then eventually to the city, to a larger ecosystem?

Stefan: Yeah. When we really think about the impact to the business, I frankly think we are at the beginning of understanding and calculating what the value of faster and more accurate decisions really is, which are enabled by AI. I don't think we have a very complete understanding at this point, and it's fairly obvious to everybody that digitalizing like the design process and the manufacturing process. It not only saves R&D effort and R&D money, but it also helps optimize the supply chain inventories, the manufacturing costs, and the total cost of the new product. And that is really where different aspects of the business come together. And I would frankly say, we start to understand the immediate effects, we start to understand if I have an AI-driven quality check that will reduce my waste, so I can understand that kind of business value.

But there is a whole dimension of business value of using this optimization that really translates to the whole enterprise. And I would say there's a lot of work happening to understand these implications better. But I would say at this point, we are just at the starting point of doing this, of really understanding what can optimization of a process do for the enterprise as a whole.

Laurel: So optimization, continuous learning, continuous improvement, this makes me think of, and cars, of course, The Toyota Way, which is that seminal book that was written in 2003, which is amazing, because it's still current today. But with lean manufacturing, is it possible for AI to continuously improve that at the chip level, at the factory level, at the city to help these businesses make better decisions?

Stefan: Yeah. In my view, The Toyota Way, again, the book published in the early 2000s, with continuous improvement, in my view, continuous improvement of course always can do a lot, but there's a little bit of recognition in the last, I would say five to 10 years, somewhere like that, that continuous improvement might have hit the wall of what's possible. So there is a lot of thought since then of what is really the next paradigm for manufacturing. When you stop thinking about evolution and optimization and you think about more revolution. And one of the concepts that have been developed here is called industry 4.0, which is really the thought about turning upside down the idea of how manufacturing or how the value chain can work. And really think about what if I get two factories that are completely self-organizing, which is kind of a revolutionary step. Because today, mostly a factory is set up around a certain idea of what products it makes and when you have lines and conveyors and stuff like that, and they're all bolted to the floor. So it's fairly static, the original idea of a factory. And you can optimize it in an evolutionary way for a long time, but you'd never break through that threshold.

So the newest thought or the other concepts that are being thought about are, what if my factory consists of independent, moving robots, and the robots can do different tasks. They can transport material, or they can then switch over to holding a robot arm or a gripper. And depending on what product I throw at this factory, it will completely reshuffle itself and work differently when I come in with a very different product and it will self-organize itself to do something different. So those are some of the paradigms that are being thought of today, which of course, can only become a reality with heavy use of AI technologies in them. And we think they are really going to revolutionize at least what some kinds of manufacturing will do. Today we talk a lot about lot size one, and that customers want more options and variations in a product. So the factories that are able to do this, to really produce very customized products, very efficiently, they have to look much different.

So in many ways, I think there's a lot of validity to the approach of continuous improvement. But I think we right now live in a time where we think more about a revolution of the manufacturing paradigm.

Laurel: That's amazing. The next paradigm is revolution. Stefan, thank you so much for joining us today in what has been an absolutely fantastic conversation on the Business Lab.

Stefan: Absolutely. My pleasure. Thank you.

Laurel: That was Stefan Jockusch, vice president of strategy for Siemens Digital Industry Software, who I spoke with from Cambridge, Massachusetts, the home of MIT and MIT Technology Review, overlooking the Charles River. That's it for this episode of Business Lab. I'm your host, Laurel Ruma. I'm 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 prints, on the web, and at events online and around the world. For more information about us and the show, please check out our website at technologyreview.com. The show is available wherever you get your podcasts. If you enjoyed this episode, we hope you'll 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.

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How AI will revolutionize manufacturing - MIT Technology Review

The Soundscapes to Landscapes (S2L): Monitoring Animal Biodiversity from Space Using Citizen Scientists program is supported by $1.1 million over three years through NASAs Citizen Science for Earth Systems program. It uses citizen scientists to deploy the AudioMoths. Other birders knowledgeable in bird calls will annotate a subset of the recordings, which serve as the training data for the AI models.

This summer, Newsam also received a $90,000, one-year AI for Earth Innovation grant from Global Wildlife Conservation in partnership with Microsoft. The nonprofit relies on research to work with local communities to address the root causes of threats to wildlife.

Newsams is one of only five projects funded out of 135 applications. The grant supports AI projects that can scale quickly. The research will benefit many other projects because it is open source.

For Newsam, there are many questions about processing the data, and many technical challenges. The recordings have biophony, geophony and anthrophony noise, and the bird calls are often faint. Some species have different calls for different communications: warning calls, mating calls and others. Which one should the AI focus on?

Birds often modify their calls by changing frequency, for example, if other birds are also calling, Newsam said. I am learning a lot about bird calls.

Baligar hears the calls as something more than just bird communication.

I like to think of birds as musical instruments, he said. All the violins are orange crowned warblers, but no two violins are the same. A bird song plays different notes, and every bird likes to play a song differently every time.

Each AudioMoth gathers about 2,000 minutes of data per site. So far, the team has more than 500,000 minute-long recordings more than 8,000 hours of data from over 600 locations and terrabytes of data to manage.

However, training the AI model requires a lot of annotated data.

Deep learning is data hungry, Baligar said. The more data the better. On average, we have just 650 training clips per bird species, which is not a lot.

Newsam, who co-founded the Spatial Analysis Research Center (SpARC) at UC Merced, is an expert in image analysis and understanding.

Image and audio are sensorily very different but in the end, it is just data data that we are turning into information through several processes, he said.

Baligar did not set out to study sound or bird calls when he was a masters student. He was more interested in time-series questions. Now, audio over time is the focus of his dissertation, and potentially the basis for a company he hopes to launch after graduation.

Computer science and environmental science are two of UC Merceds growing number of strengths, said Professor Josh Viers, director of the Center for Information Technology Research in the Interest of Society at UC Merced.

Professor Newsams research is indicative of the progress UC Merced has made in attracting top talent and solving important global problems, Viers said. Shawn is a leader in developing computer science tools that interpret and integrate massive amounts of information, from Earth imagery to sound recordings, and his research is pushing the envelope on innovation in sustainability and technology. It is really exciting to see this example of artificial intelligence used to benefit wildlife conservation efforts.

Future work for the team includes trying to identify individual birds and be able to track them over their range.

If we can overcome some of the modeling challenges, Newsam said, we might be able to replace satellites with much more fine-scaled information about all kinds of wildlife.

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AI is for the Birds in a New Computer Science Project | Newsroom - UC Merced University News

Researchers at the Allen Institute for Artificial Intelligence (AI2) have created a machine learning algorithm that can produce images using only text captions as its guide. The results are somewhat terrifying but if you can look past the nightmare fuel, this creation represents an important step forward in the study of AI and imaging.

Unlike some of the genuinely mind-blowing machine learning algorithms weve shared in the pastsee here, here, and herethis creation is more of a proof-of-concept experiment. The idea was to take a well-established computer vision model that can caption photos based on what it sees in the image, and reverse it: producing an AI that can generate images from captions, instead of the other way around.

This is a fascinating area of study and, as MIT Technology Review points out, it shows in real terms how limited these computer vision algorithms really are. While even a small child can do both of these things readilydescribe an image in words, or conjure a mental picture of an image based on those wordswhen the Allen Institute researchers tried to generate a photo from a text caption using a model called LXMERT, it generated nonsense in return.

So they set out to modify LXMERT and created X-LXMERT. And while the results that X-LXMERT generates given a text caption arent exactly coherent, theyre not nonsense eitherthe general idea is usually there. Here are some example images created by the researchers using various captions:

And here are a few examples we generated by plugging various captions into a live demo they created using their model:

The above are all based on captions provided by the researchers, and most of them seem to at least contain the major concepts in each description. However, when we tried to create totally new captions based on more esoteric concepts like photographer, photography studio, or even the word camera, the results fell apart:

While the results from and limitations of X-LXMERT probably dont inspire either awe or the fear of the impending AI revolution, the groundbreaking masking technique that the researchers developed is an important first step in teaching an AI to fill in the blanks that any text description inherently leaves out.

This will eventually lead to better image recognition and computer vision, which can only help improve tasks that actually matter to the readers of this site. In other words: the better a computer is at understanding what you mean when you describe an image or image editing task, the more complex the tasks it will be able to perform on that image.

To learn more about this creation or see some more creepy AI-generated images, read the full research paper here or check out an interactive live demo of the model at this link.

(via DPReview)

Originally posted here:

This AI Generates Photos Using Only Text Captions as a Guide - PetaPixel

Technical skills and data literacy are obviously important in this age of AI, big data, and automation. But that doesn't mean we should ignore the human side of work skills in areas that robots can't do so well. I believe these softer skills will become even more critical for success as the nature of work evolves, and as machines take on more of the easily automated aspects of work. In other words, the work of humans is going to become altogether more, well, human.

9 Soft Skills Every Employee Will Need In The Age Of Artificial Intelligence (AI)

With this in mind, what skills should employees be looking to cultivate going forward? Here are nine soft skills that I think are going to become even more precious to employers in the future.

1. Creativity

Robots and machines can do many things, but they struggle to compete with humans when it comes to our ability to create, imagine, invent, and dream. With all the new technology coming our way, the workplaces of the future will require new ways of thinking making creative thinking and human creativity an important asset.

2. Analytical (critical) thinking

As well as creative thinking, the ability to think analytically will be all the more precious, particularly as we navigate the changing nature of the workplace and the changing division of labor between humans and machines. That's because people with critical thinking skills can come up with innovative ideas, solve complex problems and weigh up the pros and cons of various solutions all using logic and reasoning, rather than relying on gut instinct or emotion.

3. Emotional intelligence

Also known as EQ (as in, emotional IQ), emotional intelligence describes a person's ability to be aware of, control, and express their own emotions and be aware of the emotions of others. So when we talk about someone who shows empathy and works well with others, were describing someone with a high EQ. Given that machines cant easily replicate humans ability to connect with other humans, it makes sense that those with high EQs will be in even greater demand in the workplace.

4. Interpersonal communication skills

Related to EQ, the ability to successfully exchange information between people will be a vital skill, meaning employees must hone their ability to communicate effectively with other people using the right tone of voice and body language in order to deliver their message clearly.

5. Active learning with a growth mindset

Someone with a growth mindset understands that their abilities can be developed and that building skills leads to higher achievement. They're willing to take on new challenges, learn from their mistakes, and actively seek to expand their knowledge. Such people will be much in demand in the workplace of the future because, thanks to AI and other rapidly advancing technologies, skills will become outdated even faster than they do today.

6. Judgement and decision making

We already know that computers are capable of processing information better than the human brain, but ultimately, it's humans who are responsible for making the business-critical decisions in an organization. It's humans who have to take into account the implications of their decisions in terms of the business and the people who work in it. Decision-making skills will, therefore, remain important. But there's no doubt that the nature of human decision making will evolve specifically, technology will take care of more menial and mundane decisions, leaving humans to focus on higher-level, more complex decisions.

7. Leadership skills

The workplaces of the future will look quite different from today's hierarchical organizations. Project-based teams, remote teams, and fluid organizational structures will probably become more commonplace. But that won't diminish the importance of good leadership. Even within project teams, individuals will still need to take on leadership roles to tackle issues and develop solutions so common leadership traits like being inspiring and helping others become the best versions of themselves will remain critical.

8. Diversity and cultural intelligence

Workplaces are becoming more diverse and open, so employees will need to be able to respect, understand, and adapt to others who might have different ways of perceiving the world. This will obviously improve how people interact within the company, but I think it will also make the businesss services and products more inclusive, too.

9. Embracing change

Even for me, the pace of change right now is startling, particularly when it comes to AI. This means people will have to be agile and cultivate the ability to embrace and even celebrate change. Employees will need to be flexible and adapt to shifting workplaces, expectations, and required skillsets. And, crucially, they'll need to see change not as a burden but as an opportunity to grow.

Bottom line: we needn't be intimated by AI. The human brain is incredible. It's far more complex and more powerful than any AI in existence. So rather than fearing AI and automation and the changes this will bring to workplaces, we should all be looking to harness our unique human capabilities and cultivate these softer skills skills that will become all the more important for the future of work.

AI is going to impact businesses of all shapes and sizes across all industries. Discover how to prepare your organization for an AI-driven world in my new book, The Intelligence Revolution: Transforming Your Business With AI.

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9 Soft Skills Every Employee Will Need In The Age Of Artificial Intelligence (AI) - Forbes

What do you get if you mix Nvidia Corp.s artificial intelligence smarts with VMware Inc.s virtualization and cloud expertise? Attendees at VMworld 2020 virtual found out when thepartners announced the release of a jointly engineered solution that promises to bring AI to every enterprise.

This is a great moment in time where AI has finally come to life, because the hardware and software has come together to make it possible, said Manuvir Das (pictured, right), head of enterprise computing at Nvidia Corp.

Das and Krish Prasad (pictured, left), senior vice president and general manager of the Cloud Platform Business Unit at VMware Inc., joined John Furrier, host of theCUBE, SiliconANGLE Medias livestreaming studio, during VMworld. They discussed the partnership between Nvidia and VMware, as well as the democratization of AI.(* Disclosure below.)

Nvidiais more usually associated with graphics processing units than AI, but the company recently closed a deal to buy Arm Holdings Inc. in a move it described as creating the worlds premier computing company for the age of AI.

VMware has been on a transformation journey of its own, morphing from offering a platform for running virtual machines into a hybrid cloud management platform that can run either Kubernetes or VM workloads on-premises or in the cloud, or clouds. This vastly simplifies the operational complexity that our customers have to deal with, Prasad said. The next chapter in VMwares journey is doing the same thing for AI workloads he added.

There is some real deep computer science here between the engineers at VMware and Nvidia, said Das, describing how the technology works. He suggested imagining the process as a three-layer stack: The foundation is the hardware to run the algorithms, which Nvidia has with its GPUs. On top is the AI-enabled software stack with all the right algorithmics that take advantage of that hardware, Das stated. This is actually where Nvidia spends most of its effort today.

Providing the middle layer that marries the software and hardware is the VMware platform. Wire these three components together with the right algorithms and you get real acceleration, according to Das.

Early use-case examples come from the healthcare field, where cancer detection has been increased exponentially through the application of AI. The workload is running 30 times faster than it was running before this integration, Das stated.

Prasad concluded: We think that this is going to vastly accelerate the adoption of AI and essentially democratize AI in the enterprise.

Watch the complete video interview below, and be sure to check out more of SiliconANGLEs and theCUBEs coverage of VMworld. (* Disclosure: VMware Inc. sponsored this segment of theCUBE. Neither VMware nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Show your support for our mission with our one-click subscription to our YouTube channel (below). The more subscribers we have, the more YouTube will suggest relevant enterprise and emerging technology content to you. Thanks!

Support our mission: >>>>>> SUBSCRIBE NOW >>>>>> to our YouTube channel.

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VMware and Nvidia make the power of AI accessible to every enterprise - SiliconANGLE News

New York, Sept. 30, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "North America Artificial Intelligence in Healthcare Diagnosis Market Forecast to 2027 - COVID-19 Impact and Regional Analysis by Diagnostic Tool ; Application ; End User ; Service ; and Country" - https://www.reportlinker.com/p05974389/?utm_source=GNW

The healthcare industry has always been a leader in innovation.The constant mutating of diseases and viruses makes it difficult to stay ahead of the curve.

However, with the help of artificial intelligence and machine learning algorithms, it continues to advance, creating new treatments and helping people live longer and healthier.A study published by The Lancet Digital Health compared the performance of deep learning a form of artificial intelligence (AI) in detecting diseases from medical imaging versus that of healthcare professionals, using a sample of studies carried out between 2012 and 2019.

The study found that, in the past few years, AI has become more precise in identifying disease diagnosis in these images and has become a more feasible source of diagnostic information.With advancements in AI, deep learning may become even more efficient in identifying diagnosis in the coming years.

Moreover, it can help doctors with diagnoses and notify when patients are weakening so that the medical intervention can occur sooner before the patient needs hospitalization. It can save costs for both the hospitals and patients. Additionally, the precision of machine learning can detect diseases such as cancer quickly, thus saving lives.In 2019, the medical imaging toolsegment accounted for a larger share of the North America artificial intelligence in healthcare diagnosis market. Its growth is attributed to an increasing adoption of AI technology for diagnosis of chronic conditions is likely to drive the growth of diagnostic tool segment in the North America artificial intelligence in healthcare diagnosis.In 2019, the radiology segment held a considerable share of the for North America artificial intelligence in healthcare diagnosis market, by the application. This segment is also predicted to dominate the market by2027 owing to rising demand for AI based application for radiology.A few major primary and secondary sources for the artificial intelligence in healthcare diagnosis market included US Food and Drug Administration, and World Health Organization.Read the full report: https://www.reportlinker.com/p05974389/?utm_source=GNW

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The North America artificial intelligence in healthcare diagnosis market is projected to reach from US$ 1,716.42 million in 2019 to US$ 32,009.61...

We arent what we were in the 50s and 60s and 70s, former Secretary of Defense Ash Carter recently reflected. In those days, all technology of consequence for protecting our people, and all technology of any consequence at all, came from the United States and came from within the walls of government. Those days are irrevocably lost. To get that technology now, Ive got to go outside the Pentagon no matter what, Carter added.

The former Pentagon chief may be overstating the case, but when it comes to artificial intelligence, theres no doubt that the private sector is in command. Around the world, nations and their governments rely on private companies to build their AI software, furnish their AI talent, and produce the AI advances that underpin economic and military competitiveness. The United States is no exception.

With Big Techs titans and endless machine-learning startups racing ahead on AI, its easy to imagine that the public sector has little to contribute. But the federal governments choices on R&D policy, immigration, antitrust, and government contracting could spell the difference between growth and stagnation for Americas AI industry in the coming years. Meanwhile, as AI booms in other countries, diplomacy and trade policy can help the United States and its private sector take greatest advantage of advances abroad, and protective measures against industrial espionage and unfair competition can help keep America ahead of its adversaries.

Smart policy starts with situational awareness. To achieve the outcomes they intend and avoid unwanted distortions and side effects in the market, American policymakers need to understand where commercial AI activity takes place, who funds it and carries it out, which real-world problems AI companies are trying to solve, and how these facets are changing over time. Our latest research focuses on venture capital, private equity, and M&A deals from 2015 through 2019, a period of rapid growth and differentiation for the global AI industry.

Although the COVID-19 pandemic has since disrupted the market, with implications for AI that are still unfolding, studying this period helps us understand the foundations of todays AI sectorand where it may be headed.

America leads, but doesnt dominate

Contrary to narratives that Beijing is outpacing Washington in this field, the United States remains the leading destination for global AI investments. China is making meaningful investments in AI, but in a diverse, global playing field it is one player among many.

As of the end of 2019, the United States had the worlds largest investment market in privately held AI companies, including startups as well as large companies that arent traded on stock exchanges. We estimate AI companies attracted nearly $40 billion globally in disclosed investment in 2019 alone, as shown in Figure 1. American companies attracted the lions share of that investment: $25.2 billion in disclosed value (64% of the global total) across 1,412 transactions. (These disclosed totals significantly understate U.S. and global investment, since many deals and deal values are undisclosed, so total transaction values were probably much higher.)

Around the world, private-market AI investment grew tremendously from 2015 to 2019especially outside China. Notwithstanding occasional claims in the media that China is outstripping U.S. investment in AI, we find that Chinese investment levels in fact continue to lag behind the United States. Consistent with broader trends in Chinas tech sector, the Chinese AI market saw a dramatic boom from 2015 to 2017, prompting many of those media claims. But the following two years, investment sharply declined, resulting in little net growth in the annual level of investment from 2015 to 2019.

Figure 1: Total disclosed value of equity investments in privately held AI companies, by target region

Although Americas nearest rival for AI supremacy may not have taken the lead, our data suggest the United States shouldnt grow complacent. Americas AI companies remain ahead in overall transaction value, but they account for a steadily shrinking percentage of global transactions. And by our estimates, investment outside the United States and China is quickly expanding, with Israel, India, Japan, Singapore, and many European countries growing faster than their larger competitors by some or all metrics.

Figure 2: Investment activity and growth in the top 10 target countries (ranked by disclosed value)

Chinese investors play a meaningful but limited role

Chinas investments abroad are attracting mounting scrutiny, but in the American AI investment market, Chinese investors are relatively minor players. In 2019, we estimate that disclosed Chinese investors participated in 2% of investments into American AI companies, down from a peak of only 5% in 2016. As Figure 3 makes clear, the Chinese investors in our dataset generally seem to invest in Chinese AI companies instead.

Figure 3: Investment events with at least one Chinese investor participant, by target region

There was also little evidence in our data that disclosed Chinese investors seek out especially sensitive companies or technologies, such as defense-related AI, when they invest outside China. That said, our data are limited; some Chinese investors may be undisclosed or operate through foreign subsidiaries that obscure their interests. And aggregate trends are of course only one part of the picture. Some China-based investors clearly invest abroad in order to extract security-sensitive information or technology. These efforts deserve scrutiny. But overall, it seems that disclosed Chinese investors, and any bad actors among them, are a relatively small piece of a larger and more diverse AI investment market.

Few AI companies focus on public-sector needs

When it comes to specific applications, we found that most AI companies are focused on transportation, business services, or general-purpose applications. There are some differences across borders: Compared to the rest of the world, investment into Chinese AI companies is concentrated in transportation, security and biometrics (including facial recognition), and arts and leisure, while in the United States and other countries, companies focused on business uses, general-purpose applications, and medicine and life sciences attract more capital.

Across all countries, though, relatively few private-market investments seem to be flowing to companies that focus squarely on military and government AI applications. Even the related category of security and biometrics is relatively small, though materially larger in China. Governments can and do adapt commercial AI tools for their own purposes, but for the time being, relatively few AI startups seem to be working and raising funds with public-sector clients in mind, especially outside China.

Figure 4: Regional investment targets by application area

The bottom-line on global AI

The worlds AI landscape is changing fast, and a plethora of unpredictable geopolitical factors, from U.S.-China decoupling to COVID-related disruptions, counsel against confident claims about where the global AI landscape is headed next. Still, our estimates of investment around the world point to fundamental, longer-term trends unlikely to vanish anytime soon. These trends have important implications for policy:

Go here to read the rest:

What investment trends reveal about the global AI landscape - Brookings Institution

Americans encounter some form of artificial intelligence and machine learning technologies in nearly every aspect of daily life: We accept Netflixs recommendations on what movie we should stream next, enjoy Spotifys curated playlists and take a detour when Waze tells us we can shave eight minutes off of our commute.

And it turns out that were fairly comfortable with this new normal: A survey released last year by Innovative Technology Solutions found that, on a scale of 1 to 10, Americans give their GPS systems an 8.1 trust and satisfaction score, followed closely by a 7.5 for TV and movie streaming services.

But when it comes to higher stakes, were not so trusting. When asked about whether they trust an AI doctor diagnosing or treating a medical issue, respondents scored it just a 5.4.

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Overall skepticism about medical AI and ML is nothing new. In 2012, we were told that IBMs AI-powered Watson was being trained to recommend treatments for cancer patients. There were claims that the advanced technology could make medicine personalized and tailored to millions of people living with cancer. But in 2018, reports surfaced that indicated the research and technology had fallen short of expectations, leaving users to speculate the accuracy of Watsons predictive analytics.

RELATED:Investors poured $4B into healthcare AI startups in 2019

Patients have been reluctant to trust medical AI and ML out of fear that the technology would not offer a unique or personalized recommendation based on individual needs. A piece in Harvard Business Review in 2019 referenced a survey in which 200 business students were asked to take a free health assessment to perform a diagnosis40% of students signed up for the assessment when told their doctor would perform the diagnosis, while only 26% signed up when told a computer would perform the diagnosis.

These concerns are not without basis. Many of the AI and ML approaches that are being used in healthcare todaydue to simplicity and ease of implementationstrive for performance at the population-level by fitting to the characteristics most common among patients. They look to do well in the general case, failing to serve large groups of patients and individuals with unique health needs. However, this limitation of how AI and ML is being applied is not a limitation of the technology.

If anything, what makes AI and ML exceptionalif done rightis its ability to process huge sets of data comprising a diversity of patients, providers, diseases and outcomes and model the fine-grained trends that could potentially have a lasting impact on a patients diagnosis or treatment options. This ability to use data in the large for representative populations and to obtain inferences in the small for individual-level decision support is the promise of AI and ML. The whole process might sound impersonal or cookie-cutter, but the reality is that the advancements in precision medicine and delivery will make care decisions more data-driven and thus more exact.

Consider a patient choosing a specialist. Its anything but data-driven: Theyll search for a provider in-network or maybe one that is conveniently located, without understanding potential health outcomes as a result of their choice. The issue is that patients lack the proper data and information they need to make these informed choices.

RELATED:The unexpected ways AI is impacting the delivery of care, including for COVID-19

Thats where machine intelligence comes into playan AI/ML model that is able to accurately predict the right treatment, at the right time, by the right provider for a patient, which could drastically help reduce the rate of hospitalizations and emergency room visits.

As an example, research published last month in AJMC looked at claims data from 2 million Medicare beneficiaries between 2017 and 2019 to evaluate the utility of ML in the management of severe respiratory infections in community and post-acute settings. The researchers found that machine intelligence for precision navigation could be used to mitigate infection rates in the post-acute care setting.

Specifically, at-risk individuals who received care at skilled nursing facilities (SNFs) that the technology predicted would be the best choice for them had a relative reduction of 37% for emergent care and 36% for inpatient hospitalizations due to respiratory infections compared to those who received care at non-recommended SNFs.

This advanced technology has the ability to comb through and analyze an individuals treatment needs and medical history so that the most accurate recommendations can be made based on that individuals personalized needs and the doctors or facilities available to them. In turn, matching a patient to the optimal provider has the ability to drastically improve health outcomes while also lowering the cost of care.

We now have the technology where we can use machine intelligence to optimize some of the most important decisions in healthcare. The data show results we can trust.

Zeeshan Syed is the CEO and Zahoor Elahi is the COO of Health at Scale.

Link:

Industry VoicesAI doesn't have to replace doctors to produce better health outcomes - FierceHealthcare

To do this, Admiral Seguros is using an AI solution, developed by the technology company Tractable, which accurately evaluates vehicle damage with photos sent through a web application. The app, via the AI, completes the complex manual tasks that an advisor would normally perform and produces a damage assessment in seconds, often without the need for further review.

Upon receiving the assessment, Admiral Seguros will use it to make immediate payment offers to policyholders when appropriate, allowing them to resolve claims in minutes, even on the first call.

Jose Maria Perez de Vargas, Head of Customer Management at Admiral Seguros, said: "Admiral Seguros continues to advance in digitalisation as a means to provide a better service to our policyholders, providing them with an easy, secure and transparent means of evaluating damages without the need for travel, achieving compensation in a few hours. It's a simple, innovative and efficient claims management process that our clients will surely appreciate."

Adrien Cohen, co-founder and president of Tractable, said: "By using our AI to offer immediate payments, Admiral Seguros will resolve many claims almost instantly, to the delight of its customers. This is central to our mission of using Artificial Intelligence to accelerate recovery, converting the process from weeks to minutes."

Tractable's AI uses deep learning for computer vision, in addition to machine learning techniques. The AI is trained with many millions of photographs of vehicle damage, and the algorithms learn from experience by analyzing a wide variety of different examples. Tractable's technology can be applied globally to any vehicle.

The AI enables insurers to assess car damage, shares recommended repair operations, and guides the claims management process to ensure these are processed and settled as quickly as possible.

According to Admiral Seguros, the application of this technology in the insurance sector will be a great step in digitization and will offer a great improvement in the customer experience of Admiral's insurance brands in Spain, Qualitas Auto and Balumba.

About Tractable:

Tractable develops artificial intelligence for accident and disaster recovery. Its AI solutions have been deployed by leading insurers across Europe, North America and Asia to accelerate accident recovery for hundreds of thousands of households. Tractable is backed by $55m in venture capital and has offices in London, New York City and Tokyo.

About Admiral Seguros

In Spain, Admiral Group plc has been based in Seville since 2006 thanks to the creation of Admiral Seguros. More than 700 people work from there and for the entire national territory, cementing and marketing their two commercial brands: Qualitas Auto, and Balumba.

Recognized as the third best company to work for in Spain, the sixth in Europe and the eighteenth in the world by the consultancy Great Place to Work, Admiral Seguros is committed to a corporate culture focused on people.

SOURCE Tractable

https://tractable.ai

Originally posted here:

Admiral Seguros Is The First Spanish Insurer To Use Artificial Intelligence To Assess Vehicle Damage - PRNewswire

YUMA PROVING GROUND, Ariz. After weeks of work in the oppressive Arizona desert heat, the U.S. Army carried out a series of live fire engagements Sept. 23 at Yuma Proving Ground to show how artificial intelligence systems can work together to automatically detect threats, deliver targeting data and recommend weapons responses at blazing speeds.

Set in the year 2035, the engagements were the culmination of Project Convergence 2020, the first in a series of annual demonstrations utilizing next generation AI, network and software capabilities to show how the Army wants to fight in the future.

The Army was able to use a chain of artificial intelligence, software platforms and autonomous systems to take sensor data from all domains, transform it into targeting information, and select the best weapon system to respond to any given threat in just seconds.

Army officials claimed that these AI and autonomous capabilities have shorted the sensor to shooter timeline the time it takes from when sensor data is collected to when a weapon system is ordered to engaged from 20 minutes to 20 seconds, depending on the quality of the network and the number of hops between where its collected and its destination.

We use artificial intelligence and machine learning in several ways out here, Brigadier General Ross Coffman, director of the Army Futures Commands Next Generation Combat Vehicle Cross-Functional Team, told visiting media.

We used artificial intelligence to autonomously conduct ground reconnaissance, employ sensors and then passed that information back. We used artificial intelligence and aided target recognition and machine learning to train algorithms on identification of various types of enemy forces. So, it was prevalent throughout the last six weeks.

Promethean Fire

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The first exercise featured is informative of how the Army stacked together AI capabilities to automate the sensor to shooter pipeline. In that example, the Army used space-based sensors operating in low Earth orbit to take images of the battleground. Those images were downlinked to a TITAN ground station surrogate located at Joint Base Lewis McCord in Washington, where they were processed and fused by a new system called Prometheus.

Currently under development, Prometheus is an AI system that takes the sensor data ingested by TITAN, fuses it, and identifies targets. The Army received its first Prometheus capability in 2019, although its targeting accuracy is still improving, according to one Army official at Project Convergence. In some engagements, operators were able to send in a drone to confirm potential threats identified by Prometheus.

From there, the targeting data was delivered to a Tactical Assault Kit a software program that gives operators an overhead view of the battlefield populated with both blue and red forces. As new threats are identified by Prometheus or other systems, that data is automatically entered into the program to show users their location. Specific images and live feeds can be pulled up in the environment as needed.

All of that takes place in just seconds.

Once the Army has its target, it needs to determine the best response. Enter the real star of the show: the FIRES Synchronization to Optimize Responses in Multi-Domain Operations, or FIRESTORM.

What is FIRESTORM? Simply put its a computer brain that recommends the best shooter, updates the common operating picture with the current enemy situation, and friendly situation, admissions the effectors that we want to eradicate the enemy on the battlefield, said Coffman.

Army leaders were effusive in praising FIRESTORM throughout Project Convergence. The AI system works within the Tactical Assault Kit. Once new threats are entered into the program, FIRESTORM processes the terrain, available weapons, proximity, number of other threats and more to determine what the best firing system to respond to that given threat. Operators can assess and follow through with the systems recommendations with just a few clicks of the mouse, sending orders to soldiers or weapons systems within seconds of identifying a threat.

Just as important, FIRESTORM provides critical target deconfliction, ensuring that multiple weapons systems arent redundantly firing on the same threat. Right now, that sort of deconfliction would have to take place over a phone call between operators. FIRESTORM speeds up that process and eliminates any potential misunderstandings.

In that first engagement, FIRESTORM recommended the use of an Extended-Range Cannon Artillery. Operators approved the algorithms choice, and promptly the cannon fired a projectile at the target located 40 kilometers away. The process from identifying the target to sending those orders happened faster than it took the projectile to reach the target.

Perhaps most surprising is how quickly FIRESTORM was integrated into Project Convergence.

This computer program has been worked on in New Jersey for a couple years. Its not a program of record. This is something that they brought to my attention in July of last year, but it needed a little bit of work. So we put effort, we put scientists and we put some money against it, said Coffman. The way we used it is as enemy targets were identified on the battlefield FIRESTORM quickly paired those targets with the best shooter in position to put effects on it. This is happening faster than any human could execute. It is absolutely an amazing technology.

Dead Center

Prometheus and FIRESTORM werent the only AI capabilities on display at Project Convergence.

In other scenarios, a MQ-1C Gray Eagle drone was able to identify and target a threat using the on-board Dead Center payload. With Dead Center, the Gray Eagle was able to process the sensor data it was collecting, identifying a threat on its own without having to send the raw data back to a command post for processing and target identification. The drone was also equipped with the Maven Smart System and Algorithmic Inference Platform, a product created by Project Maven, a major Department of Defense effort to use AI for processing full motion video.

According to one Army officer, the capabilities of the Maven Smart System and Dead Center overlap, but placing both on the modified Gray Eagle at Project Convergence helped them to see how they compared.

With all of the AI engagements, the Army ensured there was a human in the loop to provide oversight of the algorithms' recommendations. When asked how the Army was implementing the Department of Defenses principles of ethical AI use adopted earlier this year, Coffman pointed to the human barrier between AI systems and lethal decisions.

So obviously the technology exists, to remove the human right the technology exists, but the United States Army, an ethical based organization thats not going to remove a human from the loop to make decisions of life or death on the battlefield, right? We understand that, explained Coffman. The artificial intelligence identified geo-located enemy targets. A human then said, Yes, we want to shoot at that target.

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Inside the Army's futuristic test of its battlefield artificial intelligence in the desert - C4ISRNet

Does artificial intelligence require moral values? We spoke to Patricia Churchland, neurophilosopher and author of Conscience: The Origins of Moral Intuition

This article is part of the philosophy of artificial intelligence, a series of posts that explore the ethical, moral, and social implications of AI today and in the future

Can artificial intelligence learn the moral values of human societies? Can an AI system make decisions in situations where it must weigh and balance between damage and benefits to different people or groups of people? Can AI develop a sense of right and wrong? In short, will artificial intelligence have a conscience?

This question might sound irrelevant when considering todays AI systems, which are only capable of accomplishing very narrow tasks. But as science continues to break new grounds, artificial intelligence is gradually finding its way into broader domains. Were already seeing AI algorithms applied to areas where the boundaries of good and bad decisions are not clearly defined, such as criminal justice and job application processing.

In the future, we expect AI to care for the elderly, teach our children, and perform many other tasks that require moral human judgement. And then, the question of conscience and conscientiousness in AI will become even more critical.

With these questions in mind, I went in search of a book (or books) that explained how humans develop conscience and give an idea of whether what we know about the brain provides a roadmap for conscientious AI.

A friend suggested Conscience: The Origins of Moral Intuitionby Dr. Patricia Churchland, neuroscientist, philosopher, and professor emerita at the University of California, San Diego. Dr. Churchlands book, and a conversation I had with her after reading Conscience, taught me a lot about the extent and limits of brain science. Conscience shows us how far weve come to understand the relation between the brains physical structure and workings and the moral sense in humans. But it also shows us how much more we must go to truly understand how humans make moral decisions.

It is a very accessible read for anyone who is interested in exploring the biological background of human conscience and reflect on the intersection of AI and conscience.

Heres a very quick rundown of what Conscience tells us about the development of moral intuition in the human brain. With the mind being the main blueprint for AI, better knowledge of conscience can tell us a lot about what it would take for AI to learn the moral norms of human societies.

Conscience is an individuals judgment about what is normally right or wrong, typically, but not always, reflecting some standard of a group to which the individual feels attached, Churchland writes in her book.

But how did humans develop the ability to understand to adopt these rights and wrongs? To answer that question, Dr. Churchland takes us back through time, when our first warm-blooded ancestors made their apparition.

Birds and mammals are endotherms: their bodies have mechanisms to preserve their heat. In contrast, in reptiles, fish, and insects, cold-blooded organisms, the body adapts to the temperature of the environment.

The great benefit of endothermy is the capability to gather food at night and to survive colder climates. The tradeoff: endothermic bodies need a lot more food to survive. This requirement led to a series of evolutionary steps in the brains of warm-blooded creatures that made them smarter. Most notable among them is the development of the cortex in the mammalian brain.

The cortex can integrate diverse signals and pull out abstract representation of events and things that are relevant to survival and reproduction. The cortex learns, integrates, revises, recalls, and keeps on learning.

The cortex allows mammals to be much more flexible to changes in weather and landscape, as opposed to insects and fish, who are very dependent on stability in their environmental conditions.

But again, learning capabilities come with a tradeoff: mammals are born helpless and vulnerable. Unlike snakes, turtles, and insects, which hit the ground running and are fully functional when they break their eggshells, mammals need time to learn and develop their survival skills.

And this is why they depend on each other for survival.

The brains of all living beings have a reward and punishment system that makes sure they do things that support their survival and the survival of their genes. The brains of mammals repurposed this function to adapt for sociality.

In the evolution of the mammalian brain, feelings of pleasure and pain supporting self-survival were supplemented and repurposed to motivate affiliative behavior, Churchland writes. Self-love extended into a related but new sphere: other-love.

The main beneficiary of this change are the offspring. Evolution has triggered changes in the circuitry of the brains of mammals to reward care for babies. Mothers, and in some species both parents, go to great lengths to protect and feed their offspring, often at a great disadvantage to themselves.

In Conscience, Churchland describes experiments on the biochemical reactions of the brains of different mammals reward social behavior, including care for offspring.

Mammalian sociality is qualitatively different from that seen in other social animals that lack a cortex, such as bees, termites, and fish, Churchland writes. It is more flexible, less reflexive, and more sensitive to contingencies in the environment and thus sensitive to evidence. It is sensitive to long-term as well as short-term considerations. The social brain of mammals enables them to navigate the social world, for knowing what others intend or expect.

The brains of humans have the largest and most complex cortex in mammals. The brain of homo sapiens, our species, is three times as large as that of chimpanzees, with whom we shared a common ancestor 5-8 million years ago.

The larger brain naturally makes us much smarter but also has higher energy requirements. So how did we come to pay the calorie bill? Learning to cook food over fire was quite likely the crucial behavioral change that allowed hominin brains to expand well beyond chimpanzee brains, and to expand rather quickly in evolutionary time, Churchland writes.

With the bodys energy needs supplied, hominins eventually became able to do more complex things, including the development of richer social behaviors and structures.

So the complex behavior we see in our species today, including the adherence to moral norms and rules, started off as a struggle for survival and the need to meet energy constraints.

Energy constrains might not be stylish and philosophical, but they are as real as rain, Churchland writes in Conscience.

Our genetic evolution favored social behavior. Moral norms emerged as practical solutions to our needs. And we humans, like every other living being, are subject to the laws of evolution, which Churchland describes as a blind process that, without any goal, fiddles around with the structure already in place. The structure of our brain is the result of countless experiments and adjustments.

Between them, the circuitry supporting sociality and self-care, and the circuitry for internalizing social norms, create what we call conscience, Churchland writes. In this sense your conscience is a brain construct, whereby your instincts for caring, for self and others, are channeled into specific behaviors through development, imitation, and learning.

This is a very sensitive topic and complicated, and despite all the advances in brain science, many of the mysteries of the human mind and behavior remain unlocked.

The dominant role of energy requirements in the ancient origin of human morality does not mean that decency and honesty must be cheapened. Nor does it mean that they are not real. These virtues remain entirely admirable and worthy to us social humans, regardless of their humble origins. They are an essential part of what makes us the humans we are, Churchland writes.

In Conscience, Churchland discusses many other topics, including the role of reinforcement learning in the development of social behavior and the human cortexs far-reaching capacity to learn by experience, to reflect on counterfactual situations, develop models of the world, draw analogies from similar patterns and much more.

Basically, we use the same reward system that allowed our ancestors to survive, and draw on the complexity of our layered cortex to make very complicated decisions in social settings.

Moral norms emerge in the context of social tension, and they are anchored by the biological substrate. Learning social practices relies on the brains system of positive and negative reward, but also on the brains capacity for problem solving, Churchland writes.

After reading Conscience, I had many questions in mind about the role of conscience in AI. Would conscience be an inevitable byproduct of human-level AI? If energy and physical constraints pushed us to develop social norms and conscientious behavior, would there be a similar requirement for AI? Does physical experience and sensory input from the world play a crucial role in the development of intelligence?

Fortunately, I had the chance to discuss these topics with Dr. Churchland after reading Conscience.

What is evident from Dr. Churchlands book (and other research on biological neural networks), physical experience and constraints play an important role in the development of intelligence, and by extension conscience, in humans and animals.

But today, when we speak of artificial intelligence, we mostly talk about software architectures such as artificial neural networks. Todays AI is mostly disembodied lines of code that run on computers and servers and process data obtained by other means. Will physical experience and constraints be a requirement for the development of truly intelligent AI that can also appreciate and adhere to the moral rules and norms of human society?

Its hard to know how flexible behavior can be when the anatomy of the machine is very different from the anatomy of the brain, Dr. Churchland said in our conversation. In the case of biological systems, the reward system, the system for reinforcement learning is absolutely crucial. Feelings of positive and negative reward are essential for organisms to learn about the environment. That may not be true in the case of artificial neural networks. We just dont know.

She also pointed out that we still dont know how brains think. In the event that we were to understand that, we might not need to replicate absolutely every feature of the biological brain in the artificial brain in order to get some of the same behavior, she added.

Churchland reminded that while initially, the AI community largely dismissed neural networks, they eventually turned out to be quite effective when their computational requirements were met. And while current neural networks have limited intelligence in comparison to the human brain, we might be in for surprises in the future.

One of the things we do know at this stage is that mammals with cortex and with reward system and subcortical structures can learn things and generalize without a huge amount of data, she said. At the moment, an artificial neural network might be very good at classifying faces by hopeless at classifying mammals. That could just be a numbers problem.

If youre an engineer and youre trying to get some effect, try all kinds of things. Maybe you do have to have something like emotions and maybe you can build that into your artificial neural network.

One of my takeaways from Conscience was that humans generally align themselves with the social norms of their society, they also challenge them at times. And the unique physical structure of each human brain, the genes we inherit from our parents and the later experiences that we acquire through our lives make for the subtle differences that allow us to come up with new norms and ideas and sometimes defy what was previously established as rule and law.

But one of the much-touted features of AI is its uniform reproducibility. When you create an AI algorithm, you can replicate it countless times and deploy it in as many devices and machines as you want. They will all be identical to the last parametric values of their neural networks. Now, the question is, when all AIs are equal, will they remain static in their social behavior and lack the subtle differences that drive the dynamics of social and behavioral progress in human societies?

Until we have a much richer understanding of how biological brains work, its really hard to answer that question, Churchland said. We know that in order to get a complicated result out of a neural network, the network doesnt have to have wet stuff, it doesnt have to have mitochondria and ribosomes and proteins and membranes. How much else does it not have to have? We dont know.

Without data, youre just another person with an opinion, and I have no data that would tell me that youve got to mimic certain specific circuitry in the reinforcement learning system in order to have an intelligent network.

Engineers will try and see what works.

We have yet to learn much about human conscience, and even more about if and how it would apply to highly intelligent machines. We do not know precisely what the brain does as it learns to balance in a headstand. But over time, we get the hang of it, Churchland writes in Conscience. To an even greater degree, we do not know what the brain does as it learns to find balance in a socially complicated world.

But as we continue to observe and learn the secrets of the brain, hopefully we will be better equipped to create AI that serves the good of all humanity.

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Will artificial intelligence have a conscience? - TechTalks