Joseph Jude

What makes us, humans, remarkable? Is it our physical strength? Or is it our intelligence? Perhaps our desire to live in communities? Some of our cousins in the animal kingdom have all those qualities. So what differentiates us?

Our early ancestors were limited in their ability to learn life-skills for two reasons: they had to be physically around those who knew the skills, and they had to learn only through signs. When we started to speak, learning became easier and faster. When we invented writing, ideas spread wider. Hence language is the differentiating feature, Prof Toby Walsh argues in this book.

Even with speech and writing, learning suffers in a vital way. Speaker translates thought to speech; likewise, the learner turns hearing to thought. Information is lost in multiple translations. Digital companies like Tesla and Apple have eliminated such loss of information. They can transmit what one device learns to millions of other devices as code. Every device immediately knows what every other device learns and without the need for translation.

Professor Walsh expands this idea to imagine the next species in our homo family: Homo digitalis. He says homo digitalis will be both biological and digital, living both in our brains and in the broader digital space.

In exploring the parallel living deeper, he debunks the possibility of singularity the anticipated time when we will have an intelligent machine that can cognitively improve itself. Only futurists and philosophers believe the singularity is inevitable. On the other hand, AI researchers think we will have machines that reach human-level intelligence, surpassing our intelligence in several narrow domains. Experts believe we would have such machines by around 2062, which explains the title of the book. The book focuses on the impact such machines will have on our lives. Interwoven into this focus are the steps we can take now to shape that future better than today.

Professor Walsh deals with ramifications of AI-augmented humans on work, privacy, politics, equality, and wars. He paints neither a dystopian nor a utopian future. As an academician, he carefully constructs his arguments on research, data, and trends.

When we ponder artificial intelligence, we conjure up an intellect superior to humans. What might come as a surprise is that machine intelligence would be artificial. Take flight, for example. We fly, but not like birds. Machines might simulate a storm, but it wont get wet. So values and fairness determined by devices will be unnatural. This should motivate us to take steps to shape a better future, one that isnt unnatural.

The book excels in painting a realistic picture of an AI-based future. However, it falters in the steps we can take to avoid a disastrous fate. The author promotes government regulation as a primary tool to control AI. Did UN-based regulations prevent monstrous state actors from acquiring chemical weapons and using it on their citizens? As and when lethal automatic weapons are manufactured, how difficult would it be for determined non-state actors to obtain them? Another mistake is considering AI as a singular technology. Governments should regulate AI not at the input side collecting, storing, and processing data, but where it is used loan processing, police departments, weapons, and so on.

There is no question that technology as powerful as AI should be regulated. But regulation alone wont work. We need a comprehensive approach that involves academia, citizen activists, steering groups and task forces. The Internet is one example around us.

One data shown in the book should hit Indians the hardest. I wish it forms the wake-up call for policymakers, entrepreneurs, and concerned citizens. Tianjin, a city in China, outspends India in AI. The best time to invest in AI was yesterday, the next best time is now.

Continued here:

Toby Wals' 2062: The World that AI made - The Tribune

About two years after it was created, the Pentagons artificial intelligence center is setting its sights on new projects, including one on joint information warfare.

This initiative seeks to deliver an information advantage to the Department of Defense in two ways. The first is improving the DoDs ability to integrate commercial and government AI solutions. The second is improving the standardization of foundational DoD data needed to field high-performing AI-enabled capabilities to support operations in the information environment, said Lt. Cmdr. Arlo Abrahamson, a spokesman for the Joint Artificial Intelligence Center.

Nand Mulchandani, the JAICs acting director, told reporters in early July that this initiative also includes cyber operations both broad defensive and offensive measures for use by U.S. Cyber Command.

The DoD is discovering that it needs ways to process, analyze and act upon the vast amounts of data it receives.

As we look at the ability to influence and shape in this environment, were going to have to have artificial intelligence and machine-learning tools, specifically for information ops that hit a very broad portfolio, Gen. Richard Clarke, commander of Special Operations Command, said at the Special Operations Forces Industry Conference in May. Were going to have to understand how the adversary is thinking, how the population is thinking, and work in these spaces in time of relevance. If youre not at speed, you wont be relevant.

To make sure the U.S. message and our allies and partner message is being heard and its resonating. What we need is adapting data tech that will actually work in this space and we can use it for our organization.

A program in support of network incident detection, called MADHAT or Multidimensional Anomaly Detection fusing HPC, Analytics, and Tensors is helping the JAIC develop an information warfare capability. The program allows for the exploration of network data as a way of enabling more effective detection of nuanced adversarial threats, Abrahamson said.

MADHAT has already been deployed, he added, and analysts working on the High Performance Computing Modernization Program are being trained on the tool for operational use. This program accelerates technology development and transitions it into defense capabilities through the application of high-performance computing.

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Mulchandani also told reporters that other information warfare-related efforts include using natural language processing, which involve processing and analyzing text.

NLP and speech-to-text is actually a fairly mature AI technology that can be deployed in production. And that actually is going to be used in reducing information overload, he said. So being able to scan vast quantities of open-source information and bring the sort of nuggets and important stuff on the NLPs.

Link:

Pentagon AI team sets sights on information warfare - C4ISRNet

Digital transformation continues to multiply the potential attack surface exponentially, bringing new opportunities for the cyber-criminal community. In addition to their expanding arsenal of sophisticated malware and zero day threats, AI and machine learning are new tools being added to their toolbox. To the surprise of almost no-one, AI is being weaponized by cyber adversaries.

Leveraging AI and automation enables bad actors to commit more attacks at a faster rate and that means security teams are going to have to likewise quicken their speed to keep up. Adding fuel to the fire, this is happening in real-time, and were seeing rapid development, so there is little time for deciding whether to deploy your own AI countermeasures.

AI offers cyber actors more bang for the buck

Just like their victims, cyber actors are subject to economic realities: zero day threats can cost upwards of six figures to identify and exploit; developing new threats and malware takes time and can be expensive, as can renting Malware as a Service tools off the dark web. Like anyone else, they are looking to get the most bang for their buck, that means getting the most ROI with the least amount of overhead expenditure, including money, time, and effort, while maximizing the efficiency and efficacy of the tools theyre using.

Using AI and ML enables cyber-criminals to create malware that can self-seek for vulnerabilities and then autonomously determine which payloads will be the most successful without exposing itself through constant communications back to its C2 server.

We have already seen multi-vector attacks combined with advanced persistent threats (APTs) or an array of payloads. AI accelerates the effectiveness of these tools by autonomously learning about targeted systems so attacks can be laser focused rather than taking the usual slower, scattershot approach that can alert a victim that they are under attack.

AI reduces time to breach

We can all expect attacks to become faster than ever before, especially as technologies such as 5G connections are added to networks. 5G also enables edge devices to communicate faster, creating ad hoc networks that are harder to secure and easier to exploit. This can lead to swarm-based attacks where individual elements perform a specific function as part of a larger, coordinated attack.

When you incorporate AI into a network of connected devices that can communicate at 5G speeds, you create a scenario where those devices can not only launch an attack on their own, but customize that attack at digital speeds based on what it learns during the attack process.

With swarm technology, intelligent swarms of bots can share information and learn from each in other in real-time. By incorporating self-learning technologies, cyber-criminals can create attacks capable of quickly assessing vulnerabilities and then apply methods of countering efforts to stop them.

AI-based cyber-attacks will be more affordable

Traditional cyber weapons built by humans can be complex to build. Because of this, they can sell for a lot of money on the dark web. With AI in place, bad actors will be able to build weapons far more quickly, in greater quantity, and with more flexibility than ever before.

This will decrease their black market value, while at the same time, these AI-based weapons will be more plentiful and readily available to a greater number of people. In the age-old battle of quality versus quantity, threat actors will no longer need to choose: quantity will increase while quality will improve as well.

AI is AIs greatest enemy

Solutions that use AI-based strategies are the only effective defense against AI-enhanced attack strategies. However, AI takes time often years and specialized skills to develop and train. It is far more than the specialized scripts many vendors label as AI, because not everyone understands what goes into a legitimate AI solution, enterprises looking to fight fire with fire can be left in a quandary as to which solutions they should select.

This decision is critical, as future cyber battles may evolve into Flash Wars where interactions between defensive and adversarial AI systems become so fast that the attack/defense cycle is over in microseconds. Like traditional stock traders trying to compete against systems that can bid for stocks using algorithms and AI/ML models, network security professionals do not want to have to compete without having the right tools in place.

Preparing now for the coming challenges

Swarm-based network attacks are still likely a couple of years away, but the impact of AI-enhanced threats are right around the corner. Enterprises need to start preparing now for this reality and it starts with basic cybersecurity hygiene. This is about more than just having a patching and updating program in place, it also includes having proper security architectures and segmentation in place to reduce a companys attack surface and prevent hackers from gaining access to the wider system.

Collaboration is another key component to combatting the weaponization of AI. Security solutions need to be able to see and share threat intelligence, and participate in a unified and coordinated response to a detected threat, even across different network ecosystems such as multi-cloud environments.

Deception is another important tool to add to your arsenal, and which will increase in importance as attacks become faster and more sophisticated. Its essentially counterintelligence deploying decoys across the network to lure in attackers and unmask them because theyre unable to tell which assets are real and which are fake.

AI gives security teams the upper hand in the cyber arms race

As threat actors gain decreased latency and more intelligent attack resources, security teams will have to respond with even greater speed and intelligence. Humans alone cannot respond to these coming threats, and neither can the traditional security solutions they have in place. Instead, defensive strategies will have to incorporate advanced automation technology, including ML and AI.

Ultimately, enterprises have far more resources available to them than cyber-criminals do. Teams that can incorporate technologies like machine learning and AI into their cyber defenses will be able to build the quintessential security system that will not only able them to survive, but for the first time ever, gain the upper hand in the escalating cyber war.

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Delving Into the Weaponization of AI - Infosecurity Magazine

By Cuneyt Buyukbezci

Increasing automation and digitization is inevitable. More companies are transferring their operations to IT systems, and more of these operations are being automated.

However, what isnt inevitable is the rise in IT failures and periods of downtime that digitization and automation entail. Businesses are losing billions of dollars per year from IT downtime.

Fortunately, the increasing use of AI-based predictive analytics can root out problems before they even arise.

First, lets just get a firm handle on the scale of the problem, and just how much of the economy is being digitized and automated. Almost 80 percent of companies in the United States are in the process of digital transformation, meaning that 80 percent of American businesses are turning increasingly to IT systems to handle and execute various aspects of their work. And theyre pumping lots of money into this process of change: According to a recent study from Reports and Data, the global digital transformation market was valued at $261.9 billion in 2018, while its estimated to reach $1.051 trillion by 2026.

In other words, massive shifts are taking place around the world as businesses come to depend more on IT systems and digital platforms. At the same time, much of the functioning of these systems and platforms is being automated. A report from Deloitte published this year found that 58 percent of organizations globally have introduced some form of automation into their work processes, while the number of companies implementing automation at scale has doubled over the last year. This is another monumental change, indicating that as companies move to IT systems, theyre also moving towards automating much of what these systems do.

This is all very exciting, but unfortunately, this shift has caused an exponential rise in opportunities for IT failures and downtime. As more processes are put on some kind of computer system, and as more of these processes are executed by algorithms, then inevitably more chances for faults and breakdowns arise, particularly as staff are ill-equipped to monitor everything an increasingly automated system does. Indeed, estimates of the costs of downtime in lost revenue went from $26.5 billion globally in 2011to $700 billion in 2016 (and only for North American firms).

Things are getting out of hand, and one of the main reasons why many firms havent been able to solve this challenge is because theyve approached it with the wrong mentality. Generally, theyve been developing and using tools to detect IT problems as and when they appear. This might sound fine at first glance, but waiting for problems to arise can be dangerous, since they can sometimes take a long time to resolve.

For instance, the UK Parliaments Treasury Committee released a report in October complaining about the spate of IT bank failures that had been occurring in Britain over the last few years, and about how these had left millions of customers locked out of their accounts as the institutions concerned struggled to restore their systems. One of the worst examples of this occurred in 2018 when an IT outage affecting Lloyds Bank resulted in 1.9 million customers being locked out of their accounts for weeks, with the underlying problems taking several months to completely resolve.

To avoid such disasters, businesses should really take a proactive approach to their IT systems. Specifically, they need to focus on preventing problems from materializing in the first place, so that they arent left with periods of downtime that end up hurting their bottom lines. Artificial intelligence is the key to achieving this.

AI-based detection platforms are capable of monitoring IT systems in real-time, checking for early signs of potential failures. To take one example, my company Appnomic has managed to handle 250,000 severe IT incidents for our clients with AI, which equals more than 850,000 man-hours of work.

By harnessing machine learning, such platforms can use past data to learn how problems typically develop, enabling a company to step in before anything unfortunate occurs. In 2017, Gartner coined the term artificial intelligence systems for IT operations (AIOps) to describe this kind of AI-driven predictive analysis, and the market research firm believes that the use of AIOps will grow considerably over the next few years. In 2018, only 5 percent of large enterprises are using AIOps, but the firm estimates that by 2023 this figure is set to rise to 30 percent.

This growth will be driven by the fact that several benefits come from the application of machine learning and data science to IT systems. Aside from detecting likely problems before they occur, AI can significantly reduce false alarms, in that it can gain a more reliable grasp of what actually leads to failures than previous technologies and human operators. On top of this, it can detect anomalies that wont necessarily lead to failures or downtime, but that may be making an IT system less efficient.

This is why AI analytics will make IT systems more resilient and robust overall. And as more companies migrate to AIOps and related platforms they will create a snowball effect, forcing their competitors to either join the race to avoid unnecessary downtime or be left behind. And it makes perfect sense that, as automation in IT systems increases, there should be a parallel increase in automated predictive analytic systems. Because as software eats the world and we humans become less central to our own jobs, its only AI that can keep up with AI.

About the author

Cuneyt Buyukbezci is the Chief Marketing Officer of Appnomic. Cuneyt has a history of working at big enterprises running marketing, product strategy, and sales leadership at HP Software, Sun Microsystems, and CA Technologies. Hes currently helping enterprises learn how they can adopt a preemptive approach to IT management, instead of firefighting after systems fail.

Excerpt from:

How AI and predictive analytics eliminate costly IT downtime - TechTalks

This spring, much of the healthcare industry hoped that artificial intelligence could be a key tool in stemming the spread of the COVID-19 pandemic across the world.

But the results weren't just underwhelming. In some cases, they were "anti-constructive," said Dr. Isaac Kohane, chair of the Department of Biomedical Informatics at Harvard Medical School, during a FutureMed presentation on Thursday.

"We in healthcare were shooting for the moon, but we hadn't gotten out of our own backyard," said Kohane.

HIMSS20 Digital

In the United Statesthere were several attempts to use aggregate data from electronic health records. Kohane used Epic as an example, pointing to its system to predict severity of disease based on admissions.

"It didn't perform very well at all," said Kohane.

According to Kohane, a lack of high-quality data contributed to the shortfall.

"Most of the data that was being shared for the first three months was literally just case counts and death counts," he said. "To the extent that there was sharing of clinical courses, it was from single institutions," rather than interstate efforts.

"We did not have a real collective intelligence," he said.

But hope isn't completely lost for AI's role in addressing the pandemic. Kohane noted that companies are using it to develop vaccines specifically, using large databases of protein interactions and docking simulations to figure out the best protein domain to block.

In December or sooner, he said, we'll see "the results of Phase 2 trials from purely machine-learned trials."

U.S. Food and Drug Administration Principal Deputy Commissioner Amy Abernethy said during the presentation that AI might be used to help sort through the available drugs and to help get data sets cleaned up "to better understand how drugs are performing."

Meanwhile, Eran Segal, a professor in the computer science department at the Weizmann Institute of Science, pointed to the use of AI in conjunction with surveys to help predict, based on reported symptoms, which individuals should be tested.

Ultimately, said Dr. Karen DeSalvo, chief health officer at Google Health and former National Coordinator for Health IT, those building AI tools to confront the pandemic must not replicate existing biases in medicine, a possibility that is a continued concern among many developers.

"There's a really important challenge to look at fairness: to make sure that whatever we are building is not going to exacerbate inequities in health outcomes," said DeSalvo.

Kat Jercich is senior editor of Healthcare IT News.Twitter: @kjercichHealthcare IT News is a HIMSS Media publication.

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Why AI fell short in slowing the spread of COVID-19 - Healthcare IT News

The Internet of Things has become a ubiquitous buzzword today. Backed by the added horsepower of artificial intelligence, we are living in an age of intelligent and interconnected systems that have enormous predictive capabilities. From smart devices that surround us in our home to the fitness wearables we wear during our daily runs, the convergence of AI and IoT is gaining velocity. Especially for consumer brands, AIoT is the key to understand the evolving aspirations of customers, pushing the boundaries of innovation, and unlocking a world of opportunities and possibilities.

Some emerging trends that we see taking shape are:

Healthy and safe living, at your fingertips

It is safe to predict that, even when the pandemic subsides, social-distancing will be deeply embedded in our consciousness owing to its drastic impact. As a result, brands will increasingly introduce AIoT solutions that will enable consumers to maintain social hygiene and well-being. We are looking at a world of products that will guide us toward a holistic lifestylefrom smart sensors in homes that monitor air pollutants and signals an alarming rise to occupants to fitness trackers which alert you to maintain a safe distance from your fellow joggers at all times.

A new definition of connected living

There is always a risk of exposure to the virus while operating physical objects. With contactless becoming a way of life, electronic brands will have to shift gears to meet emerging needs. Imagine that as you step into your home, smart lights, and smart plugs switch on immediately, thanks to the voice command you send through Alexa. Automated heating sensors fitted in your car or home can adjust the temperature according to the surrounding environment, without you having to regulate it manually.

Television dons the role of a smart hub

Smart TVs, since their introduction in the early 2000s, have lent a new patina of ease and convenience to everyday life. Not only has the genre found popularity with users browsing social media platforms, streaming music, listening to podcasts, video calling en masse, but advanced tech features such as 4K UHD streaming has also found favour. Soon, we will see smart TVs becoming new smart hubs, through which consumers can operate and control multiple smart AI and IoT devices.

Mapping the digitally savvy shopper

The rules of engagement in retail are changing. As we see a surge of omnichannel shopping in a post-pandemic future, brands will find innovative ways to attract consumers and retain lasting loyalty. With intelligent solutions and AI-driven data analysis, brands will be able to understand consumption patterns, lifestyle goals, immediate requirements, and cater to them at a more personalised level, bringing products and services to the doorstep of the consumer with high agility and nimbleness. Retailers are already using RFID and beacon technologies to offer highly curated, personalised experiences for shoppers in-store.

With the hitherto accepted definition of a new normal changing, brands need to make targeted innovation a lynchpin of their consumer outreach strategy. The lethal combination of artificial intelligence and IoT will help garner vast pools of data at a granular level, using which brands can get a 360, deep-dive view into consumers of the future.

Views are personal. The author is CEO, realme India, and vice president, realme.

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The lethal combination of AI and IoT - Fortune India

Curi Bio said it has acquired Dana Solutions, a company that specializes in the application of artificial intelligence and machine learning to in vitro cell-based assays. The deal was for an undisclosed sum.

Seattle, WA-based Curi will gain access to Danas AI/ML-based platforms including PhenoLearn, a deep learning platform for modeling cell and tissue phenotypes; Pulse, an automated platform for contractility analysis of beating cardiomyocytes; and PhenoTox, a deep learning platform for predictive safety pharmacology.

Curis human iPSC-based platforms help drug developers build predictive and mature human iPSC tissuesespecially for the discovery, safety testing, and efficacy testing of new therapeuticswith a focus on cardiac, skeletal muscle, and neuromuscular disease models. Curi seeks to de-risk and expedite the development of new drugs by providing human-relevant preclinical data and decreasing the industrys dependence on animal models, which often fail to translate to humans.

Curi Bio is developing human-relevant platforms integrating human cells, systems, and data to accelerate the discovery of new medicines, Curi CEO Michael Cho. With the acquisition of Danas AI/ML technologies for cell-based assays, Curi is now uniquely positioned to offer pharmaceutical companies an integrated platform leveraging predictive human iPSC-derived cells, tissue-specific biosystems, and AI/ML-enabled phenotypic data insights.

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Curi Bio Dips into AI with Acquisition of Dana Solutions - Medical Device and Diagnostics Industry

While the impact of AI on COVID-19 has been widely reported in the press, the impact of COVID-19 on AI has not received much attention. Three key impact areas have helped shape the use of AI in the past five months and will continue to transform advanced analytics and AI in the months and years to come.

Responding to the COVID-19 pandemic requires global cooperation among governments, international organizations and the business community, which is at the centre of the World Economic Forums mission as the International Organization for Public-Private Cooperation.

Since its launch on 11 March, the Forums COVID Action Platform has brought together 1,667 stakeholders from 1,106 businesses and organizations to mitigate the risk and impact of the unprecedented global health emergency that is COVID-19.

The platform is created with the support of the World Health Organization and is open to all businesses and industry groups, as well as other stakeholders, aiming to integrate and inform joint action.

As an organization, the Forum has a track record of supporting efforts to contain epidemics. In 2017, at our Annual Meeting, the Coalition for Epidemic Preparedness Innovations (CEPI) was launched bringing together experts from government, business, health, academia and civil society to accelerate the development of vaccines. CEPI is currently supporting the race to develop a vaccine against this strand of the coronavirus.

The speed of decision-making leads to agile data science

The spread of the pandemic first in China and South Korea and then in Europe and the United States was swift and caught most governments, companies and citizens off-guard. This global health crisis developed into an economic crisis and a supply chain crisis within weeks (demand for toilet paper and paper towels alone rose by 600-750% during the week of March 8 in the US). Less than 100,000 global confirmed cases in early March ballooned to more than 13 million by July with more than 580,000 deaths.

With business leaders needing to act quickly, the crisis provided a chance for advanced analytics and AI-based techniques to augment decision-making. While machine learning models were a natural aid, development time for machine learning models or advanced analytical models typically clock in at four to eight weeks and thats after there is a clear understanding of the scope of the use case, as well as the necessary data to train, validate and test the models. If you add use-case evaluation before model development, and model deployment after the model has been trained, you are looking at three to four months from initial conception to production deployment.

"With business leaders needing to act quickly, the crisis provided a chance for advanced analytics and AI-based techniques to augment decision-making."

For solutions in days - not weeks or months - minimum viable AI models (MVAIM) had to be developed in much shorter time frames. Using agile data science methodologies, PwC was able to compress these times significantly, building a SEIRD (Susceptible-Exposed-Infected-Recovered-Death) model of COVID-19 progression for all 50 US states in one week. We then tested, validated and deployed it in another week. Once this initial model was deployed, we extended it to all counties in the US and made the model more sophisticated.

Uncertainty about the future leads to multi-agent simulations

Uncertainty touched every aspect of life under COVID-19 from health, to behavior to economic impact - and expedited the increased adoption of advanced analytics and AI techniques. Uncertainty feeds emotional reactions such as fear, anger and frustration and such emotionally-driven behavior took precedence over rational decisions and actions, especially in the early days of the pandemic.

Uncertainty along the different dimensions made scenario planning the dominant framework for evaluating plans and decisions. Scenario analysis became the predominant paradigm for evaluating disease progression, economic downturn and recovery (e.g., V-, U-, L, W-shaped economic recoveries), as well as for management decision-making on site openings, contingency planning, demand sensing, supply chain disruptions and workforce planning. While qualitative scenario analysis is quite common in the business world, using AI-based simulations to quantitatively understand the causal linkages of different drivers and develop contingent plans of action was brought to the fore by the pandemic.

Modeling human behavior (rational and emotional) became an important aspect of the scenario analysis. For example, compliance to the stay-at-home orders was one of the primary behavioral drivers to help contain the spread of the disease as well as economic activity. As a result, agent-based modeling and simulation was one of the primary advanced analytics and AI techniques used to perform scenario analysis. Daily mobility data on how many miles were driven within each zip code in the country became a proxy for the effectiveness of the stay-at-home orders. The same data was used to model the mobility behavior of people in different parts of US as the pandemic progressed. Agent-based models were one of the best techniques to capture the time- and location-dependent variations in human behavior during the pandemic.

System dynamic modeling, another well-known modeling technique, was critical in integrating multiple decision-making domains (e.g., COVID-19 disease progression, government interventions, people behavior, demand sensing, supply disruptions etc.). Agent-based simulation has traditionally been used by the Centers for Disease Control and Prevention (CDC) and other health authorities to model disease progression and health behaviors. Both methods have been used successfully in a number of uncertain scenarios to help make strategic and operational management decisions. Some recent examples include the following:

Lack of historical data leads to upsurge of model-based AI

Given the rarity of the pandemic event there was very little historical data at a global level on the disease. As a result, there was little data to power data-rich, model-free approaches to AI, like deep learning that have become popular in recent years.

By necessity, model-based AI (which leverages the data available) saw a resurgence. As the pandemic progressed, and more data was available, data-rich and model-free approaches could be combined, leading to a few key hybrid solutions:

In many ways, the pandemic has highlighted the inadequacies of our systems, processes, governance and behaviors. On the other hand, it has also provided an opportunity for data scientists and AI scientists to put their advanced techniques and tools to use by helping business leaders make decisions in a challenging environment thats dominated by speed, uncertainty and lack of data.

In summary, as organizations manage through this pandemic and transform themselves post-pandemic three key learnings are worth keeping in mind. First, focus on agile data science methods that address the speed, urgency, and uncertainty of decision making. Second, build and manage your business using dynamic and resilient models (e.g., scenario-based simulations using system dynamic and agent-based models) that capture the inter-relationships of multiple domains (e.g., demand, production, supply, finance) and human behavior. Third, combine model-rich and data-rich approaches to obtain the best of both worlds while building AI systems. These approaches can ensure you can seek solutions quickly while maximizing the technologies and processes already in place.

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3 ways COVID-19 is transforming advanced analytics and AI - World Economic Forum

Tesla CEO Elon Musk reiterated his concerns about the future of artificial intelligence on Wednesday, saying those who don't believe a computer could surpass their cognitive abilities are "way dumber than they think they are."

"I've been banging this AI drum for a decade," Musk said. "We should be concerned about where AI is going. The people I see being the most wrong about AI are the ones who are very smart, because they can't imagine that a computer could be way smarter than them. That's the flaw in their logic. They're just way dumber than they think they are."

Musk has previously said he believes AI poses a much larger threat to humanity than nuclear weapons and called for regulations to monitor the development of AI technology.

"I think the danger of AI is much bigger than the danger of nuclear warheads by a lot," Musk said in 2018. "Nobody would suggest we allow the world to just build nuclear warheads if they want, that would be insane. And mark my words: AI is far more dangerous than nukes."

Facebook CEO Mark Zuckerberg has disagreed with Musk, saying AI has already improved health care and could reduce car accidents, while calling excessive pessimism about AI "pretty irresponsible." In response, Musk calledZuckerberg's understanding of AI "limited."

Are you a current or former Tesla employee? Do you have an opinion about what it's like to work there? Contact this reporter atmmatousek@businessinsider.com. You can also reach out on Signal at 646-768-4712 or email this reporter's encrypted address atmmatousek@protonmail.com.

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Elon Musk: Smart people who doubt AI are 'way dumber than they think' - Business Insider - Business Insider

The stock market appears strangely indifferent to Covid-19 these days, but that wasnt true in March, as the scale and breadth of the crisis hit home. By one measure, it was the most volatile month in stock market history; on March 16, the Dow Jones average fell almost 13 percent, its biggest one-day decline since 1987.

To some, the vertigo-inducing episode also exposed a weakness of quantitative (or quant) trading firms, which rely on mathematical models, including artificial intelligence, to make trading decisions.

Some prominent quant firms fared particularly badly in March. By mid-month, some Bridgewater Associates funds had fallen 21 percent for the year to that point, according to a statement posted by the companys co-chairman, Ray Dalio. Vallance, a quant fund run by DE Shaw, reportedly lost 9 percent through March 24. Renaissance Technologies, another prominent quant firm, told investors that its algorithms misfired in response to the months market volatility, according to press accounts. Renaissance did not respond to a request for comment. A spokesman for DE Shaw could not confirm the reported figure.

The turbulence may reflect a limit with modern-day AI, which is built around finding and exploiting subtle patterns in large amounts of data. Just as algorithms that grocers use to stock shelves were flummoxed by consumers sudden obsession with hand sanitizer and toilet paper, those that help hedge funds wring profit from the market were confused by the sudden volatility of panicked investors.

In finance, as in all things, the best AI algorithm is only as good as the data its fed.

Andrew Lo, a professor at MIT and the founder and chairman emeritus of AlphaSimplex, a quantitative hedge fund based in Cambridge, Massachusetts, says quantitative trading strategies have a simple weakness. By definition a quantitative trading strategy identifies patterns in the data, he says.

Lo notes that March bears similarities to a meltdown among quantitative firms in 2007, in the early days of the financial crisis. In a paper published shortly after that mini-crash, Lo concluded that the synchronized losses among hedge funds betrayed a systemic weakness in the market. What we saw in March of 2020 is not unlike what happened in 2007, except it was faster, it was deeper, and it was much more widespread, Lo says.

What we saw in March of 2020 is not unlike what happened in 2007, except it was faster, it was deeper, and it was much more widespread.

Andrew Lo, MIT

Zura Kakushadze, president of Quantigic Solutions, describes the March episode as a quant bust in an analysis of the events posted online in April.

Kakushadzes paper looks at one form of statistical arbitrage, a common method of mining market data for patterns that are exploited by quant funds through many frequent trades. He points out that even quant funds that employed a dollar-neutral strategy, meaning they bet equally on stocks rising and falling, did poorly in the rout.

In an interview, Kakushadze says the bust shows AI is no panacea during extreme market volatility. I don't care whether youre using AI, ML, or anything else, he says. Youre gonna break down no matter what.

In fact, Kakushadze suggests that quant funds that use overly complex and opaque AI models may have suffered worse than others. Deep learning, a form of AI that has taken the tech world by storm in recent years, for instance, involves feeding data into neural networks that are difficult to audit. Machine learning, and especially deep learning, can have a large number of often obscure (uninterpretable) parameters, he writes.

Ernie Chan, managing member of QTS Capital Management, and the author of several books on machine trading, agrees that AI is no match for a rare event like the coronavirus.

Its easy to train a system to recognize cats in YouTube videos because there are millions of them, Chan says. In contrast, only a few such large swings in the market have occurred before. You can count [these huge drops] on one hand. So its not possible to use machine learning to learn from those signals.

Still, some quant funds did a lot better than others during Marchs volatility. The Medallion Fund operated by Renaissance Technologies, which is restricted to employees money, has reportedly seen 24 percent gains for the year to date, including a 9 percent lift in March.

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Even the Best AI Models Are No Match for the Coronavirus - WIRED

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Adobe and Red Hat, an IBM company, have announced a strategic partnership to that includes cloud hosting under Red Hat, Adobe joining IBM's partner ecosystem and IBM extending services across Adobe's core enterprise applications. Company officials say the partnership will strengthen real-time data security for enterprises, focusing on regulated industries, and enable companies to deliver more personalized experiences across the customer journey.

The partnership will initially focus on:

As part of the partnership, IBM has named Adobe its "Global Partner for Experience" and will begin adopting Adobe Experience Cloud and its enterprise applications.

IBM once competed with Adobe in customer experience and marketing software offerings. But it sold off the former to HCL in 2018 and then six months later dumped its marketing software to a private-equity investor.

In other customer experience and marketing software news ...

Microsoft has released Microsoft Dynamics 365 Customer Voicein a move to help organizations collect and distribute feedback from customers across teams. Company officials said the software comes in the form of ready-to-use templates with default questions designed to fit feedback scenarios: periodic customer feedback, post-delivery, post-visit and post-support call. Users can customize questions in the templates to fit specific product or service needs.

Dynamics 365 Customer Voice includes built-in integrations with all Microsoft business applications including Microsoft Dynamics 365, Microsoft Power Apps and integrations to external third-party applications through Microsoft Power Automate.

Users can send a survey to customers while using integrated data from other applications, such as open rates, marketing channel preferences and product and service preferences. Survey results integrate back into business applications and are available to anyone who engages with the customer. This information can then be used to enhance customer profiles in Microsoft Dynamics 365 Customer Insights.

Users can also define their customer satisfaction (CSAT) metric and map that score to a new survey to use that metric to collect feedback periodically and track trends over time on the dashboard. Users can also set alerts on metrics to notify relevant business users when they receive poor feedback.

Google has made Recommendations AI, which is designed to help organizations drive personalized product recommendations to their customers, publicly availableto all customers in beta. Recommendations AI helps piece together the history of a customers shopping journey and serve them with personalized product recommendations. Recommendations AI also handles recommendations in scenarios with long-tail products and cold-start users and items.

It uses deep learning models and user metadata to draw insights across millions of items at scale and iterate on those insights. Recommendations AI also delivers model management experience in a managed service. Users can start using the product by creating a Google Cloud project and integrating and backfilling catalog and user events data with tools such as Merchant Center, Google Tag Manager, Google Analytics 360, Cloud Storage and BigQuery.

Qubit, which offers AI-led merchandising and personalized experiences, announced it has furthered its partnership with Google Cloud, making Recommendations AI available for ecommerce teams. The product recommendations solution is integrated within Qubits new product interface for merchandising.

Terminus, a provider of Account-Based Marketing (ABM) software, has announced its July product release and Terminus Engagement Hub. Its offerings Advertising Experiences, Email Experiences, Web Experiences and Chat Experiences are all now available in The Terminus Engagement Hub. Terminus Chat Experiences are now fully integrated into the Terminus Engagement Hub.

Users can see their target accounts and segments flow automatically into Chat Experiences; personal greetings and chats for visitors are automatically routed to the appropriate outbound rep on mobile or desktop. Chat Views is designed to helps sales and marketing teams identify qualified inbound leads and real-time sales conversations on websites with target accounts.

Salesforce has announced new Marketing Cloud innovations, including integrations with the acquired Evergage technology. New Interaction Studio innovations leverage Einstein and technology integrated from Evergage. They include:

The enterprise edition of the Salesforce Pardot marketing automation now includes new features to support complex B2B marketing teams:

Salesforce also announced a Datorama integration with Tableau to help marketers optimize their budget and data with features that include:

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Adobe Partners With Red Hat, Google Updates AI Offering and More CX News - CMSWire

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One of the biggest challenges post coronavirus lockdown has been to balance lives and livelihoods. How do factories and workplaces re-open while ensuring the safety of their employees, remains the pertinent question. As employers around the globe grapple with this, it has become evidently clear that the solution cannot be one size fits all. The way out needs a technology that could be adapted and fine-tuned to every factory floor, airport lounge and classroom. At the same time, it needs to be broad-based to meet international health and safety parameters.

In other words, the answer lies in adapting Artificial Intelligence and Internet of Things (IoT) technologies.

Also read: Big Data and AI tools of the Fourth Industrial Revolution that can help beat Covid-19

For my team at BLP Industry.AI, the first step was to understand the practical difficulties that floor managers and supervisors in factories were facing, such as the inability to monitor their employees and if they were wearing the required safety gear constantly or not. Another difficulty was in ensuring social distancing not just among employees but visitors as well. Going through the inquiries we received from about 40 companies, both domestic and multinational, we learnt that some of them wanted their employees to submit a self-declaration document every day, which included questions on their health and whether they had visited a containment zone recently. Monitoring these daily self-declarations was proving to be cumbersome.

To ensure the safety of employees, an early warning system is necessary, so that anyone running high fever can be taken off the factory or office floor immediately. But there is no way companies can regularly monitor the temperature of every employee. Also, to prevent the spread of Covid-19, contact tracing is necessary, which again is a difficult task for employers. In addition, the companies would want to protect their supply chain, in particular the micro, small and medium scale (MSME) suppliers. Now, the employers wanted to achieve all of these and in a cost-effective manner.

We focussed on developing AI and IoT-based technology solutions for industry, educational institutions, hospitals, hotels, airports, etc., and came up with three broad ones that could be adapted based on the specific needs of different industries.

Also read: Geo-mapping, CCTV cameras, AI how Telangana Police is using tech to enforce Covid safety

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The first product, Trust AI, is a cloud-based solution that uses a combination of visual analytics, mathematical, and neural network models to analyse video feed. Any existing camera is connected to the cloud or the companys server, which scans the feed in real time and immediately sends out an alert when a breach occurs.No new investment in CCTV cameras is required.

Alerts are sent to the safety officer or supervisors if safety gear usage (masks, helmets, safety jackets, etc.) or social distancing guidelines are not followed.In addition, the tool monitors hotspots in the factory and frequency of breaches so that managers can change the workforce on the floor.

Besides Covid-19, the technology can also be used in detecting fires, increasing workforce productivity, and reducing manufacturing defects. Institutions are also reducing security costs by replacing guards with computer vision models.

Also read: NIC awaits diverse enough pool of chest X-rays to develop its AI-based Covid detection model

The second product, Us Pro,is a cellphone technology meant only for enterprises and industries. It provides real time alerts on the employees cell phone when social distancing is breached. The phone sends out an instant alert to its owner, thereby providing an active defence system. Once an alert is triggered, it is recorded on the back-end AI application. This application, with relevant reports and dashboard, is only accessible to the health or safety officer of that particular factory or office.Privacy was a major factor for all the companies and therefore a number of steps were taken such as limiting the use of technology only while the employee is at the factory or office, and keeping all communications between the phones encrypted. As a result, everyone remains anonymous.

The technology also tracks the employees temperature every few hours, and alerts the safety officer if there are signs of high fever.In case the person tests Covid positive, the AI application, using contact tracing, determines who among others has a higher probability of falling ill or contracting the virus.

Also read: How to make safe decisions when you cant plan much in the age of Covid

The third solution, Spot AI,involves wearable devices such as a wrist band or an ID card, which vibrates when a worker breaches social distancing and geo-fencing norms.

The technology platform is normally used to drive operational and workforce productivity by locating and coordinating human, machine, and material flow on a factory floor.Increasingly, a number of US universities and Indian schools are evaluating the use of this technology as a non-intrusive way to create social distancing awareness.

These AI-based tools have helped companies be in a position to proactively implement safety measures in the workplace.Now, the retail sector too can use these applications to ensure that customers are adhering to safety norms such as wearing masks when in the store, or to keep a check on travellers at airports.

In crowded spaces such as offices and commercial buildings, these tools will help in protecting a large number of people if someone shows the symptoms of Covid-19. In most schools, cellphones are not allowed, so students can use wrist bands. The large Indian hotel chains that have properties across the country are evaluating a combination of these technologies. Hospitals, too, are evaluating the camera and wearable devices to keep their medical staff and patients safe.

Also read: Power consumption can explode with increasing use of artificial intelligence

There were several issues that the partner companies and Industry.AI grappled with while developing these solutions. But the three that stood out were technology, privacy, and implementational challenges.

Based on regular feedback from partner companies on how to adapt the application to real-world requirements, a number of technology challenges were overcome.

Privacy was a critical issue that was extensively debated, and mitigating steps were taken. One, the technologies are only being applied within the confines of a factory, university, hospital, or an office. All the data remains anonymous, and only the safety and health officer or the appointed administrators of that particular company have access to them.Two, the camera feed is not stored on any server. All alerts are deleted after a certain period of time as per the companys privacy policy.

As for implementation, the human resource managers and the culture of the company play a critical role. The implementation requires a good understanding of the workforces concerns and perspectives in order to ensure that the usage, scope and benefits of the technology are communicated clearly. Companies must create an environment of trust and convince their employees that it is in everyones best interest to adopt these preventive and safety measures.

Also read: Why we should not hype the hope for the Oxford-AstraZeneca Covid vaccine

It is heartening to see corporations develop partnerships and come together in a time of crisis. In this case, the partnership between a few large corporations, supported by an able technology company, and subsequent pilot programmes with other industries, resulted in scalable and frugal solutions to be tested and implemented in a short period of time by a number of factories from auto component major Lucas TVS to one of Indias largest electrical equipment companies, Havells. Moreover, some firms are now evaluating how these technologies can be used in a post-Covid world as well.

There are fears that the coronavirus pandemic may re-occur in waves, and the vaccines may not be ready for all virus mutations. This is forcing industries not only to partner with each other, but also with governments, to accelerate the adoption of next-generation technology. Given that supply chains have been broken and disrupted, we are seeing corporations accelerate their digital transformation plans to improve organisational productivity and decision-making.

As we move towards getting back to what we once considered normal, we will see the traditional paradigm being re-evaluated. And AI and Big Data will drive not only asset and employee productivity, but also the safety of the workforce.

Tejpreet Singh Chopra is the Founder and CEO of BLP Group, and former CEO of GE in India, Sri Lanka and Bangladesh. He is on the board of SRF, IEX, Anand Group, and AP Moller Maersks Pipavav port.He is a Young Global Leader of the World Economic Forum, and an Aspen Institute Fellow. Views are personal.

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How AI is helping reopen factory floors safely in a pandemic - ThePrint

It doesn't matter where your webcam is positioned, it's always going to be offset from the person you're talking to. The end result is we hardly ever look people in the eye when talking to them over video chat. Microsoft has created an intelligent solution, though.

As spotted by Liliputing, the latest Windows 10 Insider Preview Build (20175) announcement contains details of a new feature Microsoft is calling "Eye Contact." It uses artificial intelligence to "adjust your gaze on video calls so you appear to be looking directly in the camera." So there's no need to remember to look at the webcam instead of the person on your screen, which no one ever does as it's simply not natural.

The one drawback of Eye Contact, at least for now, is the fact it's limited to only working on the Surface Pro X. That's because the Pro X contains Microsoft's SQ1 ARM processor, which it developed in partnership with Qualcomm and includes the "artificial intelligence capabilities" required for the gaze adjustment to work, according to Microsoft. If you do own a Pro X and have access to this latest Windows Preview Build, then the feature can be turned on via the Surface app. After that, it should work with any video app using the webcam.

Considering Intel's x86 processors are capable of handling AI-intensive video games, it seems likely Microsoft will eventually expand the Eye Contact feature to other models of its Surface range and hopefully to Windows 10 in general. After that, we can all look at the people we are talking to on video chat and know that they are also seeing us looking directly at them, albeit thanks to AI.

Further Reading

Webcam Reviews

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Microsoft Uses AI to Make Our Eyes Look at the Webcam - PCMag