TLDR: The Ultimate Python and Artificial Intelligence Certification Bundle explore training in data science and how to build machines that think for themselves.

After 20 years as one of the undisputed kings of programming languages, Java may be about to relinquish its crown. For two decades, Java and C have held the top two spots on Tiobes programming language rankings.

After experiencing what Tiobe called an all-time low in popularity, falling over 4 percentage points in year-over-year usage rates, Java is now poised to see its no. 2 rankings usurped by the hard-charging Python.

And yes, C programming should be looking over its shoulder as well. Python and its monumental role in advanced programming technologies like machine learning and artificial intelligence have made it the fastest-growing coding discipline of the past decade.

You can learn Python from the ground up as well as some of its most important applications in The Ultimate Python and Artificial Intelligence Certification Bundle. Its now available for $39.96, over 90 percent off, from TNW Deals.

This package includes nine courses featuring almost 40 hours of training covering all things Python, from basic fundamentals through to how its used in some of the most in-demand tech fields working today.

Three courses Python: Introduction to Data Science and Machine Learning A-Z, Python for Beginners: Learn All the Basics of Python and Python For Beginners: The Basics For Python Development get the training underway with basic math concepts, data science introductions, programming dos and donts, as well as everything a new user needs to understand how and why Python works so well.

After a brief segue into a pair of courses centered around data organization and visualization using fellow data science stalwart R programming, the training then steps up to more advanced Python-related subjects: deep learning and the creation of artificial intelligence.

Keras Bootcamp for Deep Learning and AI in Python gives learners a grounding in using Keras, Googles powerful deep learning framework, to create artificial neural networks and the foundations of how machines are being constructed to think and act on their own. That learning expands in Image Processing and Analysis Bootcamp with OpenCV and Deep Learning in Python, where Python Tensorflow and Keras are used to help machines actually interpret images and extract meaning.

Deep learning models get deeper exploration in Master PyTorch for Artificial Neural Networks (ANN) and Deep Learning before learning how to speed up those processes by using H2O in Artificial Intelligence (AI) in Python: A H2O Approach.

The entire package is a nearly $1,800 collection of training, but by getting in on this bundle now, you can get each course at less than $5 each, only $39.99.

Prices are subject to change.

Read next: This highly rated Google Play Store language learning app is now on sale

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Python and artificial intelligence are the future so learn it all here for less than $5 a course - The Next Web

Machines have remodelled our lives at an ever-accelerating pace since the dawn of the industrial revolution. But the most profound revolution yet is about to occur in our working lives, thanks to the exponential influence of artificial intelligence.

And although already underway in many sectors, the robotic revolution is about to transform employment.

AI is no longer a thing of science fiction, it exists in the world and helps us with more day to day tasks than we even realise or think about

RS Components

Electrical experts at RS Components have commissioned exclusive research suggesting more than 30 percent of UK jobs are under threat from breakthroughs in cutting-edge artificial intelligence tech.

With pioneering advances in technology, many jobs initially considered unsuitable for automation suddenly are at risk.

Employers are increasingly attracted to the role robots can play, due to the increasing need for fewer people in the workplace because of the coronavirus pandemic.

READ MORE:AI-manipulated media will be WEAPONISED to trick military

RS Components incorporated Office for National Statistics and PricewaterhouseCoopers data to reveal how many jobs per sector are at risk of being taken by robots by 2030 a mere decade away.

The people most at risk of their jobs being taken over by robots are those who work in catering.

The shocking survey suggests 54 percent of jobs in this industry could soon be at risk.

Within the catering and hospitality services, tech has already revolutionised Digital Points Of Sale (POS).

These range from online food ordering apps, to brand-new tech for ordering food at the table without the need for humans.

And eateries have gone even further, such as the Boston restaurant Spyce, which has already replaced human cooks with robot chefs.

Manufacturing is another industry where robots are expected to take over.

The survey warns 45 percent of roles within this industry are also at risk approximately 1,170,000 potential jobs.

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This is because repetitive manual labour and routine tasks can be mimicked easily by fixed machines, saving employers both time and money.

Other industries which could be affected in the next decade include construction, wholesale, retail and property housing and estate management.

A sector where risk is lower for robots taking over roles is within the legal profession, where only 24 percent of jobs are at risk for now.

Although AI is able to automate some administrative tasks within law, AI is not yet going to replace lawyers anytime soon.

Instead, a more realistic view could be to reduce the hours a lawyer may need to spend on tasks, without making them entirely redundant.

An RS Spokesperson told Express.co.uk: Whilst the world we currently are living in brings with it concerns and worries surrounding job security for many, the concept of certain roles being replaced by AI is one we should try and approach in a positive manner.

Not only will this be a gradual process but a vast majority of industries will still require that vital level of human interaction.

AI is no longer a thing of science fiction, it exists in the world and helps us with more day to day tasks than we even realise or think about.

Although it may seem worrying from the outset, in reality, AI opens up huge possibilities within workplaces, including new opportunities and roles for people that, at the moment, we can't even imagine.

Technological changes may eliminate specific jobs, but historically it has created more roles in the process which is what we should focus on in this scenario."

However, the future is not all doom and gloom, as experts are increasingly confident automation is capable of boosting productivity, enabling workers to focus on higher-value, more rewarding jobs.

And wealth and spending will also be boosted by the initiation of AI seizing work.

Additionally, there are just some things artificial intelligence cannot yet learn, meaning certain sectors will be safe for many years to come.

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AI revolution: The jobs to be replaced by Artificial Intelligence in next decade REVEALED - Daily Express

C3.ai, Microsoft and Adobe have announced the launch of C3 AI CRM powered by Microsoft Dynamics 365.

The first enterprise-class, AI-first customer relationship management solution is purpose-built for industries, integrates with Adobe Experience Cloud, and drives customer-facing operations with predictive business insights.

The partners have agreed to integrate Microsoft Dynamics 365, Adobe Experience Cloud (including Adobe Experience Platform), and C3.ai's industry-specific data models, connectors, and AI models, in a joint go-to-market offering designed to provide an integrated suite of industry-specific AI-enabled CRM solutions including marketing, sales, and customer service.

The partnership will sell the industry-specific AI CRM offering through dedicated sales teams to target enterprise accounts across multiple industries globally, as well as through agents and industry partners.

They will target industry vertical markets initially including financial services, oil and gas, utilities, manufacturing, telecommunications, public sector, healthcare, defense, intelligence, automotive, and aerospace.

Additionally, the partnership will market the jointly branded offering globally, supported by the companies' commitment to customer success.

"Microsoft, Adobe, and C3.ai are reinventing a market that Siebel Systems invented more than 25 years ago," says Thomas M. Siebel, CEO of C3.ai.

"The dynamics of the market and the mandates of digital transformation have dramatically changed CRM market requirements. A general-purpose CRM system of record is no longer sufficient," he says.

"Customers today demand industry-specific, fully AI-enabled solutions that provide AI-enabled revenue forecasting, product forecasting, customer churn, next-best product, next-best offer, and predisposition to buy."

Satya Nadella, CEO at Microsoft, says, "This year has made clear that businesses fortified by digital technology are more resilient and more capable of transforming when faced with sweeping changes like those we are experiencing.

"Together with C3.ai and Adobe, we are bringing to market a new class of industry-specific AI solutions, powered by Dynamics 365, to help organisations digitise their operations and unlock real-time insights across their business," he says.

Shantanu Narayen, president and CEO of Adobe, adds, "We are proud to partner with C3.ai and Microsoft to advance the imperative for digital customer engagement.

"The unique combination of Adobe Experience Cloud, the industry-leading solution for customer experiences, together with the C3 AI Suite and Microsoft Dynamics 365, will enable brands to deliver rich experiences that drive business growth," Narayen says.

Combining Microsoft Dynamics 365 CRM software with Adobe's suite of customer experience management solutions alongside C3.ai's enterprise AI capabilities, C3 AI CRM is the world's first AI-driven, industry-specific CRM built with a modern AI-first architecture.

C3 AI CRM integrates and unifies vast amounts of structured and unstructured data from enterprise and extraprise sources into a unified, federated image to drive real-time predictive insights across the entire revenue supply chain, from contact to cash. With embedded AI-driven, industry-specific workflows, C3 AI CRM helps teams:

C3 AI CRM enables brands to take advantage of their real-time customer profiles for cross-channel journey orchestration. The joint solution offers an integrated ecosystem that empowers customers to take advantage of leading CRM capabilities along with an integrated ecosystem with Azure, Microsoft 365, and the Microsoft Power Platform.

C3 AI CRM is pre-built and configured for industries financial services, healthcare, telecommunications, oil and gas, manufacturing, utilities, aerospace, automotive, public sector, defense, and intelligence enabling customers to deploy and operate C3 AI CRM and its industry-specific machine learning models quickly.

In addition, C3 AI CRM leverages the common data model of the Open Data Initiative (ODI), making it easier to bring together disparate customer data from across the enterprise.

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Microsoft, Adobe and C3.ai re-invent CRM with artificial intelligence - IT Brief Australia

Artificial intelligence enables computers and machines to mimic the perception, learning, problem-solving, and decision-making capabilities of the human mind.

In computer science, the term artificial intelligence (AI) refers to any human-like intelligence exhibited by a computer, robot, or other machine. In popular usage, artificial intelligence refers to the ability of a computer or machine to mimic the capabilities of the human mindlearning from examples and experience, recognizing objects, understanding and responding to language, making decisions, solving problemsand combining these and other capabilities to perform functions a human might perform, such as greeting a hotel guest or driving a car.

After decades of being relegated to science fiction, today, AI is part of our everyday lives. The surge in AI development is made possible by the sudden availability of large amounts of data and the corresponding development and wide availability of computer systems that can process all that data faster and more accurately than humans can. AI is completing our words as we type them, providing driving directions when we ask, vacuuming our floors, and recommending what we should buy or binge-watch next. And its driving applicationssuch as medical image analysisthat help skilled professionals do important work faster and with greater success.

As common as artificial intelligence is today, understanding AI and AI terminology can be difficult because many of the terms are used interchangeably; and while they are actually interchangeable in some cases, they arent in other cases. Whats the difference between artificial intelligence and machine learning? Between machine learning and deep learning? Between speech recognition and natural language processing? Between weak AI and strong AI? This article will try to help you sort through these and other terms and understand the basics of how AI works.

The easiest way to understand the relationship between artificial intelligence (AI), machine learning, and deep learning is as follows:

Let's take a closer look at machine learning and deep learning, and how they differ.

Machine learning applications (also called machine learning models) are based on a neural network,which is a network of algorithmic calculations that attempts to mimic the perception and thought process of the human brain. At its most basic, a neural network consists of the following:

Machine learning models that arent deep learning models are based on artificial neural networks with just one hidden layer. These models are fed labeled datadata enhanced with tags that identify its features in a way that helps the model identify and understand the data. They are capable of supervised learning (i.e., learning that requires human supervision), such as periodic adjustment of the algorithms in the model.

Deep learning models are based on deep neural networksneural networks with multiple hidden layers, each of which further refines the conclusions of the previous layer. This movement of calculations through the hidden layers to the output layer is called forward propagation. Another process, called backpropagation, identifies errors in calculations, assigns them weights, and pushes them back to previous layers to refine or train the model.

While some deep learning models work with labeled data, many can work with unlabeled dataand lots of it. Deep learning models are also capable of unsupervised learningdetecting features and patterns in data with the barest minimum of human supervision.

A simple illustration of the difference between deep learning and other machine learning is the difference between Apples Siri or Amazons Alexa (which recognize your voice commands without training) and the voice-to-type applications of a decade ago, which required users to train the program (and label the data) by speaking scores of words to the system before use. But deep learning models power far more sophisticated applications, including image recognition systems that can identify everyday objects more quickly and accurately than humans.

For a deeper dive into the nuanced differences between thesetechnologies, read AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: Whats the Difference?

Weak AIalso called Narrow AI or Artificial Narrow Intelligence (ANI)is AI trained and focused to perform specific tasks. Weak AI drives most of the AI that surrounds us today. Narrow is a more accurate descriptor for this AI, because it is anything but weak; it enables some very impressive applications, including Apple's Siri and Amazon's Alexa, the IBM Watson computer that vanquished human competitors on Jeopardy, and self-driving cars.

Strong AI, also called Artificial General Intelligence (AGI), is AI that more fully replicates the autonomy of the human brainAI that can solve many types or classes of problems and even choose the problems it wants to solve without human intervention. Strong AI is still entirely theoretical, with no practical examples in use today. But that doesn't mean AI researchers aren't also exploring (warily) artificial super intelligence (ASI), which is artificial intelligence superior to human intelligence or ability. An example of ASI might be HAL, the superhuman (and eventually rogue) computer assistant in 2001: A Space Odyssey.

As noted earlier, artificial intelligence is everywhere today, but some of it has been around for longer than you think. Here are just a few of the most common examples:

The idea of 'a machine that thinks' dates back to ancient Greece. But since the advent of electronic computing (and relative to some of the topics discussed in this article) important events and milestones in the evolution of artificial intelligence include the following:

IBM has been a leader in advancing AI-driven technologies for enterprises and has pioneered the future of machine learning systems for multiple industries. Based on decades of AI research, years of experience working with organizations of all sizes, and on learnings from over 30,000 IBM Watson engagements, IBM has developed the AI Ladder for successful artificial intelligence deployments:

IBM Watson products and solutions give enterprises the AI tools they need to transform their business systems and workflows, while significantly improving automation and efficiency. For more information on how IBM can help you complete your AI journey, explore IBM's portfolio of managed services and solutions.

Sign up for an IBMid and create your IBM Cloud account.

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What is Artificial Intelligence (AI)? | IBM

Artificial intelligence(AI) capabilities, frommachine learninganddeep learningtonatural languageprocessing (NLP) and computer vision, are rapidly advancing. Technology has never moved at such pace, meaning the role of the CIO is harder than ever to stay current and up to date with technology overall, so understanding the vast array of AI capabilities is a stretch for most CIOs right now, says Wayne Butterfield, director of cognitive automation and innovation technology research at advisory firmISG.

Naturally, IT leaders are increasingly exploring AI applications in the enterprise. However, AI-enabled initiatives do not necessarily lend themselves to traditional IT approaches.

AI-enabled initiatives do not necessarily lend themselves to traditional IT approaches.

It is imperative for CIOs to know AI in reasonable depth to understand its realistic and pragmatic adoption, explains Yugal Joshi, vice president of digital, cloud, and application services research forEverest Group. They need to understand what is doable as of today versus 3-5 years from now. Otherwise, there is a risk of them to either overestimate or underestimate AIs impact on business as well as IT.

[ Do you understandthe main types of AI?Read also:5 artificial intelligence (AI) types, defined.]

In addition, the business appetite for AI-driven transformation is at an all-time high, even asAI-washing by technology vendorscontinues to be a very real phenomenon. Its more important than ever that CIOs be able to differentiate between what is real versus what is vendor-driven AI marketing to make the best decisions for their business, Joshi says.

CIOs are increasingly hiring AI-savvy IT pros to further their digital transformation efforts. But those team members are depending on their IT leaders to understand enough about AI to best support and sustain their efforts. To that end, here are nine things CIOs should understand about AI.

In actual fact, its a group of technologies used to solve specific problems, says Butterfield. The catch-all term of Artificial Intelligence is so genericthat it is almost meaningless. In the most simplistic terms, AI is usually geared around providing a data-based answer or providing a data-fueled prediction. Then things begin to diverge.

NLP may be used to automate incoming emails, machine vision to gauge quality on the product line, or advanced analytics to predict a failure of your network. (For more on the various flavors of AI, read5 AI types, defined.) CIOs need to at least understand the strands of AI that are relevant to their business and ensure that they have a basic understanding of the problems that AI can solve for their business, and those it will not, Butterfield says.

"There is certainly a wide variety of people's expectations of AI, from realistic to off-the-wall."

There is certainly a wide variety of peoples expectations of AI, from realistic to off-the-wall, says Timothy Havens, the William and Gloria Jackson Associate Professor of Computer Systems in theCollege of Computing at Michigan Technological Universityand director of theInstitute of Computing and Cybersystems. CIOs should have at least a decent understanding of the limitations of AI such that they can predicate their expectations and properly evaluate AI solutions they are considering.

Machine learning, for example, can produce implicit models of very complex processes from representative data or experience. So an ML algorithm can learn to recognize cats by looking at millions of pictures of cats and not-cats, but it will not learn that cats meow or eat kibble.

The ROI on AI requires more patience than your average IT initiative. An Everest Group survey of more than 200 global IT leaders 84 percent cited long wait to return as a challenge. CIOs need to realize the reasons behind these long waits rather than getting flustered and disappointed with these, Joshi says.

In some cases, there may not be sufficient data governance in place.

CIOs need to understand the amount of data crunching needed to create an intelligent system, says Joshi. Therefore, CIOs need to decide whether the business has data and capability to build or use an AI system.

Havens advises CIOs to always ask where the training data will come from and how an algorithm is evaluated. That gets at whether this algorithm has been proven on real-world data that it hasnt seen before, Havens says.

In some cases, there may not be sufficient data governance in place. Although most organizations claim data is important, few invest as if that is the case. Their other enterprise functions such as HR and Finance have much larger teams than their data practice, says Joshi. CIOs need to understand what skills they need to invest given their spend appetite as some data skills may not be affordable for enterprises.

There is often a debate of where data science or AI Centers of Excellence belong, says Dan Simion, vice president of AI & Analytics with Capgemini North America. Some CIOs believe data scientists should sit within IT, while others may suggest data scientists be embedded within the business. CIOs must ensure that they are not downplaying the role of data scientists, says Simion, noting that when used properly they can do more than descriptive data visualizations but also solve business problems by leveraging AI and machine learning technologies.

CIOs who want to unlock the full potential of their AI programs should realize the knowledge and skills of their data scientists and give them opportunities to maximize the value they can drive, Simion says.

Thus, the operations team becomes extremely critical to the success or failure of intelligent capabilities. In fact, 61 percent of enterprises said their operations team are leaders in the charge of AI adoption in their organization, according to Everest Group research.

The operations team becomes extremely critical to the success or failure of intelligent capabilities.

Though [an increasing number of] enterprises are leveraging cloud-based AI offerings for cloud and SaaS vendors, the operations team is critical to scale such initiatives and create the needed guardrails, Joshi says.

One of the IT leaders most important roles is understanding the technology requirements necessary to support and sustain the companys AI transformations. In order for a company to be successful along its AI journey, Simion says, the CIO needs to make sure the AI technology stack is working and in sync with the overall enterprise technology.

Unlike many historical IT projects, AI initiatives require collaboration across data analytics, infrastructure, applications, data management, and the business. CIOs need to have the vision for creating such pod-based cross-functional teams that are jointly held accountable for the outcome and not for their individual pieces, Joshi says.

Although we throw around the term intelligent, AI is not inherently adaptive. AI algorithms are only good at what they are designed for, and will often fail miserably and in strange ways when applied to problems that may seem similar to humans, but are not similar from an AI-perspective, Havens says. An algorithm that is trained to drive a car in an urban environment may and probably will fail at rural driving, for example.

Is your organization looking to increase efficiency? Improve effectiveness? Transform the customer or user experience? Create entirely new business models? The CIO must understand what value the business wants to derive from AI adoption. Everest Group notes four common business imperatives: Efficiency, Effectiveness, Experience, and Evolution. CIOs may also need to manage inflated expectations of business around AI adoption and its impact on the organization.

[ How can automation free up staff time for innovation? Get the free eBook:Managing IT with Automation. ]

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Artificial Intelligence (AI): 9 things IT pros wish the CIO knew - The Enterprisers Project

Artificial intelligence is the new oil, and the governments or the countries that get the best datasets will unquestionably develop the best AI, the Joint Artificial Intelligence Center's chief technologyofficer said Oct. 15.

Speaking on a panel about AI superpowers at the Politico AI Summit, Nand Mulchandani said AI is a very large technology and industry. "It's not a single, monolithic technology," he said. "It's a collection of algorithms, technologies, etc., all cobbled together to call AI."

The United States has access to global datasets, and that's why global partnerships are so incredibly important, he said, noting the Defense Department launched the AI partnership for defense at the JAIC recently to have access to global datasets with partners, which gives DOD a natural advantage in building these systems at scale.

"Industry has to develop on its own, and that's where the global talent is; that's where the money is; that's where all of the innovation is going on," Mulchandani noted, adding that the U.S. government's job is to be able to work in the best way and absorb the best technology that it can. That includes working hand in glove with industry on a voluntary basis, he said. He said there are certain areas of AI that are highly scaled that you can trust and deploy at scale.

"But notice many or not many of those systems have been deployed on weapon systems. We actually don't have any of them deployed," he said.

Mulchandani said the reason is that explainability, testing, trust and ethics are all highly connected pieces and even AI security when it comes to model security, data security being able to penetrate and break models. This is all very early, which is why the DOD and the U.S. government widely have taken a very stringent approach to putting together the ethics principles and frameworks within which we're going to operate.

"[Earlier this year, one of the first international visits that we made were to NATO and our European partners, and [we] then pulled them into this AI partnership for defense that I just talked about," he said. "Thirteen different countries are getting together to actually build these principles because we actually do need to build a lot of confidence in this."

He said DOD continues to attract and have the best talent at JAIC. "The real tricky part is: How do we actually take that technology and get it deployed? That's the complexity of integrating AI into existing systems, because one isn't going to throw away the entire investment of legacy systems that one has, whether it be software or hardware or even military hardware," Mulchandani said. "[How] can we absorb the best of what's coming and get it integrated into the system as where the complexity is?"

DOD has had a long history of companies that know how to do that, and harnessing it is the actual work and the piece that we're worried about the most and really are focused on the most, he added.

A global workforce the DOD technology companies are global companies, he emphasized. "These are not linked to a particular geographic region. We hire. We bring the best talent in, wherever it may be, [and we have] research and development arms all over the world."

DOD has special security needs and requirements that must be taken care of when it comes to data, and the JAIC is putting in place very different development processes now to handle AI development, he said. "So, the dynamics of the way software gets built [and] the dynamics of who builds it are changing in a very significant way," Mulchandani said. "But the global war for talent is a real one, which is why we are not actually focused on trying to corner the market on talent."

He said they are trying to build leverage by building relationships with the leading AI companies to harness the innovation.

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Defense Official Calls Artificial Intelligence the New Oil - Department of Defense

By adding a few pixels (highlighted in red) to a legitimate check, fraudsters can trick artificial intelligence models into mistaking a $401 check for one worth $701. Undetected, the exploit could lead to large-scale financial fraud.

Yaron Singer climbed the tenure track ladder to a full professorship at Harvard in seven years, fueled by his work on adversarial machine learning, a way to fool artificial intelligence models using misleading data. Now, Singers startup, Robust Intelligence, which he formed with a former Ph.D. advisee and two former students, is emerging from stealth to take his research to market.

This year, artificial intelligence is set to account for $50 billion in corporate spending, though companies are still figuring out how to implement the technology into their business processes. Companies are still figuring out, too, how to protect their good AI from bad AI, like an algorithmically generated voice deepfake that can spoof voice authentication systems.

In the early days of the internet, it was designed like everybodys a good actor. Then people started to build firewalls because they discovered that not everybody was, says Bill Coughran, former senior vice president of engineering at Google. Were seeing signs of the same thing happening with these machine learning systems. Where theres money, bad actors tend to come in.

Enter Robust Intelligence, a new startup led by CEO Singer with a platform that the company says is trained to detect more than 100 types of adversarial attacks. Though its founders and most of the team hold a Cambridge pedigree, the startup has established headquarters in San Francisco and announced Wednesday that it had raised $14 million in a seed and Series A round led by Sequoia. Coughran, now a partner at the venture firm, is the lead investor on the fundraise, which also comes with participation from Engineering Capital and Harpoon Ventures.

Robust Intelligence CEO Yaron Singer is taking a leave from Harvard, where he is a professor of computer science and applied mathematics.

Singer followed his Ph.D. in computer science from the University of California at Berkeley, by joining Google as a postdoctoral researcher in 2011. He spent two years working on algorithms and machine-learning models to make the tech giants products run faster, and saw how easily AI could go off the rails with bad data.

Once you start seeing these vulnerabilities, it gets really, really scary, especially if we think about how much we want to use artificial intelligence to automate our decisions, he says.

Fraudsters and other bad actors can exploit the relative inflexibility of artificial intelligence models in processing unfamiliar data. For example, Singer says, a check for $401 can be manipulated by adding a few pixels that are imperceptible to the human eye yet cause the AI model to read the check erroneously as $701. If fraudsters get their hands on checks, they can hack into these apps and start doing this at scale, Singer says. Similar modifications to data inputs can lead to fraudulent financial transactions, as well as spoofed voice or facial recognition.

In 2013, upon taking an assistant professor position at Harvard, Singer decided to focus his research on devising mechanisms to secure AI models. Robust Intelligence comes from nearly a decade in the lab for Singer, during which time he worked with three Harvard pupils who would become his cofounders: Eric Balkanski, a Ph.D. student advised by Singer; Alexander Rilee, a graduate student; and undergraduate Kojin Oshiba, who coauthored academic papers with the professor. Across 25 papers, Singers team broke ground on designing algorithms to detect misleading or fraudulent data, and helped bring the issue to government attention, even receiving an early Darpa grant to conduct its research. Rilee and Oshiba remain involved with the day-to-day activities at Robust, the former on government and go-to-market, and the latter on security, technology and product development.

Robust Intelligence is launching with two products, an AI firewall and a red team offering, in which Robust functions like an adversarial attacker. The firewall works by wrapping around an organizations existing AI model to scan for contaminated data via Robusts algorithms. The other product, called Rime (or Robust Intelligence Machine Engine), performs a stress test on a customers AI model by inputting basic mistakes and deliberately launching adversarial attacks on the model to see how it holds up.

The startup is currently working with about ten customers, says Singer, including a major financial institution and a leading payment processor, though Robust will not name any names due to confidentiality. Launching out of stealth, Singer hopes to gain more customers as well as double the size of the team, which currently stands at 15 employees. Singer, who is on leave from Harvard, is sheepish about his future in academia, but says he is focused on his CEO role in San Francisco at the moment.

For me, Ive climbed the mountain of tenure at Harvard, but now I think weve found an even higher mountain, and that mountain is securing artificial intelligence, he says.

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This Harvard Professor And His Students Have Raised $14 Million To Make AI Too Smart To Be Fooled By Hackers - Forbes

Artificial Intelligence (AI) is transforming the world of work at a rapid pace, and its impact is poised to accelerate even more in the decades ahead. The rise of AI in the workplace has the potential to improve some aspects of work such as repetitive and dangerous tasks but much of the public discussion has focused on AIs potential to displace workers and the grave impacts this could pose to their livelihoods and quality of life.

Less discussed but equally important is how AI can also be part of the solution to train, upskill, and reskill employees, future-proofing and preparing them for the future of work.

The World Economic Forum's Future of Jobs Report 2020 found as automation increases, the amount of time spent on tasks at work by humans and machines will be equal in just five years. According to the report, 85 million jobs will be displaced across 26 countries and 15 industries by 2025, and more than 80% of employers expect to make wider use of remote work and to digitize work processes.

In other words, even with no further advances of AI, todays workers will face enormous disruptions as labour markets are transformed by automation tools already on the market.

The challenge ahead will be even greater for two reasons: First, COVID-19 has taken an enormous toll on the global economy, leading to an estimated loss of 14% of working hours or approximately 400 million full-time jobs in the second quarter of 2020.

Second, far from remaining static, automation technologies are being developed and commercialized at an accelerating pace during the pandemic, and their capacities are expanding as they harness growing quantities of data, computing power, and experience.

Despite these significant headwinds, AI holds great potential for workers and the future of labour. The Future of Jobs Report found that 97 new million jobs that are adapted to the new division of labour between humans and machines could be created by 2025.

Instead of worrying about job displacement, we should get to work creating the new, high-quality jobs of tomorrow while massively expanding our upskilling and reskilling efforts to transition displaced workers into these new opportunities. This is where AI can help solve some of the very problems it helped create.

Fortunately, a new cohort of purpose-driven AI innovators has begun mobilizing to ensure that workers around the world share not only in the risks that AI poses but also its powerful benefits.

Here are four ways that AI is helping create a just transition to a positive future of work:

1. Invest in reskilling and upskilling

Some AI start-ups are focused directly on reskilling and upskilling todays workers, using AI algorithms to create personalized training programmes that build on workers existing skillsets to prepare them for future opportunities that leverage technology. For example, California-based EdCast combines a detailed assessment of workers skills with data-driven analysis of future labour market needs, allowing users to identify potential future jobs and gain the skills and qualifications they need to secure them.

2. Embed learning into everyday activities

Other innovators are focused on embedding learning into everyday work activities so that workers are continuously growing their skillsets and expanding their capacity to fulfil future workforce needs. For example, Canadian start-up Axonify has pioneered the idea of microlearning in which employees spend three to five minutes per shift on learning and performance improvement activities that are matched to their individual needs by an AI algorithm.

3. Match workers to new opportunities

While AI-driven reskilling can help prepare workers for the disruptions underway, there is still an enormous need to match workers to new opportunities being created. AI can help here too, and start-ups like SkyHive and Kalido have created AI-driven platforms that connect workers to new opportunities within and outside their current organizations, based on their individual skillsets, career goals, and retraining needs.

4. Prepare the next generation of workers

AI is not only being used to future-proof todays workers and match them with tomorrows opportunities but also to prepare the next generation of workers for a future disrupted and transformed by technology. Recent years have seen an explosion in AI-powered education technology (EdTech) tools to improve learning outcomes at the K-12 level, primarily in the developed world but increasingly amongst less privileged populations.

For example, New Delhi-based start-up ConveGenius has targeted the 100 million students in India with the most limited access to quality teaching, using AI to assess each students learning needs and deliver the educational content they need to succeed as workers and citizens. Scores of other start-ups are racing to address this significant business challenge and seize the opportunity to upskill and reskill workers for the future.

No single technology will solve all the problems facing workers today. Achieving a just transition to a positive future of work will require smart government policies, ambitious corporate programs, and individual action by workers in addition to new technologies.

But as we work to improve our responses to the disruptions underway, we should encourage innovators and entrepreneurs to focus on new ways to use AI technologies to empower the workers of today and create exciting new opportunities for the workers of tomorrow.

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Here's how AI can train workers for the jobs of the future - World Economic Forum

An X-class solar flare from our sun in November 2013. Scientists trained a neural network to find such flares in data taken of distant planets around other stars. Credit: Scott Wiessinger, Solar Dynamics Observatory at NASA Goddard Space Flight Center

University of Chicago scientists teach a neural net to find baby star flares.

Like its human counterparts, a young star is cute but prone to temper flaresonly a stars are lethal. A flare from a star can incinerate everything around it, including the atmospheres of any nearby planets starting to form.

Finding out how often such young stars erupt can help scientists understand where to look for habitable planets. But until now, locating such flares involved poring over thousands of measurements of star brightness variations, called light curves, by eye.

Scientists with the University of Chicago and the University of New South Wales, however, thought this would be a task well suited for machine learning. They taught a type of artificial intelligence called a neural network to detect the telltale light patterns of a stellar flare, then asked it to check the light curves of thousands of young stars; it found more than 23,000 flares.

Published on October 23, 2020, in the Astronomical Journaland the Journal of Open Source Software the results offer a new benchmark in the use of AI in astronomy, as well as a better understanding of the evolution of young stars and their planets.

When we say young, we mean only a million to 800 million years old, said Adina Feinstein, a UChicago graduate student and first author on the paper. Any planets near a star are still forming at this point. This is a particularly fragile time, and a flare from a star can easily evaporate any water or atmosphere thats been collected.

NASAs TESS telescope, aboard a satellite that has been orbiting Earth since 2018, is specifically designed to search for exoplanets. Flares from faraway stars show up on TESSs images, but traditional algorithms have a hard time picking out the shape from the background noise of star activity.

NASAs Solar Dynamics Observatory captures flares from the sun. Credit: NASA

But neural networks are particularly good at looking for patternslike Googles AI picking cats out of internet imagesand astronomers have increasingly begun to look to them to classify astronomical data. Feinstein worked with a team of scientists from NASA, the Flatiron Institute, Fermi National Accelerator Laboratory, the Massachusetts Institute of Technology and the University of Texas at Austin to pull together a set of identified flares and not-flares to train the neural net.

It turned out to be really good at finding small flares, said study co-author and former UChicago postdoctoral fellow Benjamin Montet, now a Scientia Lecturer at the University of New South Wales in Sydney. Those are actually really hard to find with other methods.

Once the researchers were satisfied with the neural nets performance, they turned it loose on the full set of data of more than 3,200 stars.

They found that stars similar to our sun only have a few flares, and those flares seem to drop off after about 50 million years. This is good for fostering planetary atmospheresa calmer stellar environment means the atmospheres have a better chance of surviving, Feinstein said.

This can help scientists pinpoint the most likely places to look for habitable planets elsewhere in the universe.

In contrast, cooler stars called red dwarfs tended to flare much more frequently. Red dwarfs have been seen to host small rocky planets; If those planets are being bombarded when theyre young, this could prove detrimental for retaining any atmosphere, she said.

The results help scientists understand the odds of habitable planets surviving around different types of stars, and how atmospheres form. This can help them pinpoint the most likely places to look for habitable planets elsewhere in the universe.

They also investigated the connection between stellar flares and star spots, like the kind we see on our own suns surface. The spottiest our sun ever gets is maybe 0.3% of the surface, Montet said. For some of these stars were seeing, the surface is basically all spots. This reinforces the idea that spots and flares are connected, as magnetic events.

The scientists next want to adapt the neural net to look for planets lurking around young stars. Currently we only know of about a dozen younger than 50 million years, but theyre so valuable for learning how planetary atmospheres evolve, Feinstein said.

Reference: Flare Statistics for Young Stars from a Convolutional Neural Network Analysis of TESS Data by Adina D. Feinstein, Benjamin T. Montet, Megan Ansdell, Brian Nord, Jacob L. Bean, Maximilian N. Gnther, Michael A. Gully-Santiago, and Joshua E. Schlieder, 23 October 2020, The Astronomical Journal.DOI:10.3847/1538-3881/abac0a

Other UChicago-affiliated scientists on the study included visiting assistant research professor Brian Nord and Assoc. Prof. Jacob Bean.

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Artificial Intelligence Helps Understand the Evolution of Young Stars and Their Planets - SciTechDaily