advanced technology Archives - Artificial Intelligence

Advanced series of more robust drones are teaching themselves how to fly

aiuniverse — Sat, 14 Mar 2020 06:45:03 +0000

Source: techxplore.com

Drones, specifically quadcopters, are an adaptable lot. They’ve been used to assess damage after disasters, deliver ropes and life-jackets in areas too dangerous for ground-based rescuers, survey buildings on fire and deliver medical specimens.

But to achieve their full potential, they have to be tough. In the real world, drones are forced to navigate uncertain shapes in collapsing buildings, avoid obstacles and deal with challenging conditions, including storms and earthquakes.

At the USC Viterbi School of Engineering’s Department of Computer Science, researchers have created artificially intelligent drones that can quickly recover when pushed, kicked or when colliding with an object. The autonomous drone “learns” how to recover from a slew of challenging situations thrown at it during a simulation process.

“Currently, the controllers designed to stabilize quadcopters require careful tuning and even then, they are limited in terms of robustness to disruption and are model-specific,” said the study’s lead author Artem Molchanov, a Ph.D. in computer science candidate in USC’s Robotic Systems Embedded Laboratory.

“We’re trying to eliminate this problem and present an approach that leverages recent advancement in reinforcement learning so we can completely eliminate hand-tuning controllers and make drones super robust to disruptions.”

The paper, called “Sim-to-(Multi)-Real: Transfer of Low-Level Robust Control Policies to Multiple Quadrotors,” was presented at the International Conference on Intelligent Robots and Systems.

Co-authors were Tao Chen, USC computer science master’s student; Wolfgang Honig, a former USC computer science Ph.D. student; James A. Preiss, a computer science Ph.D. student; Nora Ayanian, USC assistant professor of computer science and Andrew and Erna Viterbi Early Career Chair; and Gaurav Sukhatme, professor of computer science and electrical and computer engineering and USC Viterbi executive vice dean.

Learning to fly

Roboticists have been turning to birds for flight inspiration for years. But drones have a long way to go before they’re as agile as their feathered counterparts. When a drone ends up in an undesirable orientation, such as upside down, it can be difficult for it to right itself. “A drone is an inherently unstable system,” said Molchanov.

“Controlling a drone requires a lot of precision. Especially when something sudden occurs, you need a fast and precise sequence of control inputs.” But, if a drone was able to learn from experience, like humans, it would be more capable of overcoming these challenges.

With this is mind, the USC researcher team created a system that uses a type of machine learning, a subset of artificial intelligence, called reinforcement learning to train the drone in a simulated environment. More precisely, to train the drone’s “brain,” or neural network controller.

“Reinforcement learning is inspired by biology—it’s very similar to how you might train a dog with a reward when it completes a command,” said Molchanov.

Of course, drones don’t get snacks. But in the process of reinforcement learning, they do receive an algorithmic reward: a mathematical reinforcement signal, which is positive reinforcement that it uses to infer which actions are most desirable.

Learning in simulation

The drone starts in simulation mode. At first, it knows nothing about the world or what it is trying to achieve, said Molchanov. It tries to jump a little bit or rotate on the ground.

Eventually, it learns to fly a little bit and receives the positive reinforcement signal. Gradually, through this process, it understands how to balance itself and ultimately fly. Then, things get more complicated.

While still in simulation, the researchers throw randomized conditions at the controller until it learns to deal with them successfully. They add noise to the input to simulate a realistic sensor. They change the size and strength of the motor and push the drone from different angles.

Over the course of 24 hours, the system processes 250 hours of real-world training. Like training wheels, learning in simulation mode allows the drone to learn on its own in a safe environment, before being released into the wild. Eventually, it finds solutions to every challenge put in its path.

“In simulation we can run hundreds of thousands of scenarios,” said Molchanov.

“We keep slightly changing the simulator, which allows the drone to learn to adapt to all possible imperfections of the environment.”

A real-world challenge

To prove their approach, the researchers moved the trained controller onto real drones developed in Ayanian’s Automatic Coordination of Teams Lab. In a netted indoor drone facility, they flew the drones and tried to throw them off by kicking and pushing them.

The drones were successful in correcting themselves from moderate hits (including pushes, light kicks and colliding with an object) 90% of the time. Once trained on one machine, the controller was able to quickly generalize to quadcopters with different dimensions, weights and sizes.

While the researchers focused on robustness in this study, they were surprised to find the system also performed competitively in terms of trajectory tracking—moving from point A to B to C. While not specifically trained for this purpose, it seems the rigorous simulation training also equipped the controller to follow a moving target precisely.

The researchers note that there’s still work to be done. In this experiment, they manually adjusted a few parameters on the drones, for example, limiting maximum thrust, but the next step is to make the drones completely independent. The experiment is a promising move towards building sturdy drones that can tune themselves and learn from experience.

Professor Sukhatme, Molchanov’s advisor and a Fletcher Jones Foundation Endowed Chair in Computer Science, said the research solves two important problems in robotics: robustness and generalization.

“From a safety perspective, robustness is super important. If you’re building a flight control system, it can’t be brittle and fall apart when something goes wrong,” said Sukhatme.

“The other important thing is generalization. Sometimes you might build a very safe system, but it will be very specialized. This research shows what a mature and accomplished Ph.D. student can achieve, and I’m very proud of Artem and the team he assembled.”

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Tickeron Announces Unprecedented AI Feature Precalculating Trading Success Odds

aiuniverse — Wed, 12 Feb 2020 07:08:50 +0000

Source: aithority.com

Tickeron, an artificial and human intelligence platform delivering unparalleled trading insights and analysis, announces its AI-driven feature generating instant historic and predictive analysis by precalculating the odds of success for self-directed investors and investment advisors.

Tickeron is a subscription-based market intelligence platform giving access to the latest trading news and AI-generated predictions. This technology equips users with exclusive data and analysis to assist with trading decisions and portfolio allocation. To enhance the user experience, the odds of success feature gives investors instant predictions based in part on a security’s historical success rate.

Tickeron backtests as many as hundreds of scenarios using artificial intelligence to predict the odds of success when investing in specific stocks, ETFs, mutual funds and other securities. These observations educate investors on similar pricing moves that have occurred or if there is historical evidence suggesting securities will move predictably within a certain market environment.

“For years, hedge funds have always had a leg-up with access to this advanced technology, allowing them to find and backtest trade ideas in seconds. But the world is changing, and technology is becoming more readily available to more people. The AI tools that hedge funds were traditionally using to get ahead are now available on the internet. Our latest feature gives every day, self-directed investors a fighting chance to close the performance gap,” stated Sergey Savastiouk, CEO and Founder of Tickeron.

Tickeron provides users with detailed charts displaying price moves and whether they indicate changes in a trend that could be a buy or sell signal for investors. The A.I.dvisor analyzes every previous case of a security to determine its success rate, not only in the one stock a user is researching, but in similar stocks as well.

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How Government is Incorporating Data Science

aiuniverse — Tue, 03 Sep 2019 10:57:37 +0000

Source:-analyticsinsight.net

Data scientists are taking on new importance as the difficulties of transforming raw data into an organisational resource has become increasingly daunting. Today, it is the data scientists as opposed to software developers who are probably going to be approached for the task. They have the knowledge and the tools to change over our monetary, operational, social and other information into valuable data, which thus helps government offices as well as other open and private sector organisations from numerous points of view.

State and local governments specifically need to make their data visible, open and helpful to its constituents. This not just makes it transparent and offers the public genuine peace of mind, it additionally enables external experts to contribute on the most proficient method to best use these tremendous amounts of data.

This procedure has already in system across various levels of government. Here are five different ways various government offices and agencies are utilizing data science.

Research in Healthcare

The National Institutes of Health (NIH) kickstarted an initiative named the Big Data to Knowledge (BD2K) to upgrade biomedical research. BD2K likewise serves to augment involvement of community and to encourage disclosure of new knowledge. It gives the ability to reap, control, and analyze biomedical big data so as to better know about the illnesses and human well-being.

The Food and Drug Administration (FDA) utilizes big data technologies in a recently launched Technology Transfer program to analyze and comprehend the patterns of foodborne diseases. It additionally utilizes data science to react all the more viably to the products which are contaminated in the food supply. The Center for Disease Control (CDC) rummages through social media to follow the spread of ailments, while the government is subsidizing a venture that can recognize the early indications of suicidal inclinations from social media behavior.

Environmental Protection

Both the CSIRO and Geoscience Australia have taken immense data management and analytical methods for the nation’s ecological protection and sustainability. Analytical simulations take into account all the more clear comprehension of environmental assets accordingly educating applicable parties on the most proficient method to best apportion and deliver water.

Likewise, the Clean Energy Regulator (CER) additionally profits by data analysis. The CER regulates schemes for measuring, overseeing, diminishing and balancing Australia’s greenhouse gases. With data simulation, it can better illuminate government strategy, meet global settlement commitments and support statistical services.

Government Education

The US Branch of Education is creating learning analytics and data mining frameworks that can screen and address an online student’s study design and identify fatigue from patterns of key snaps in real-time.

The Notice and Comment Project utilizes natural language processing and advanced analytics to track changes in laws, strategies, and guidelines in order to update the 4,000,000 or more government reports that it benefits people in general.

Fraud Detection

Fraud detection is a key territory where the government uses data management and analytics to the nation’s advantage. Alongside other government organizations, the CER reacts to fraud and non-compliance, and the Australian Electoral Commission (AEC) utilizes data simulations to explore electoral fraud.

Because of the sheer volume of data examined and watched, simulations can discover inconsistencies simpler than before. This procedure is sped along by the exceptionally gifted data scientists and experts that work across various government parts. They channel through the broad data and blend the consequences of the simulation to detect deceitful conduct in an exceptionally proficient way.

Battling Crime

The US Division of Homeland Security (DHS) is among the leading clients of data science in the government. The organization utilizes big data methodologies that incorporate interoperability to integrate and think about data from different security offices so as to anticipate or detect potential dangers to the nation.

The CIA-financed Palantir Technologies is in charge of an analytical software that battles terrorism by finding roadside bombs and battles cyber fraud by following transactions showing fake like patterns. All law authorization offices approach the Automated License Plate Recognition (ALPR) that recognizes vehicles claimed by individuals with remarkable warranties. Predictive tech likewise interfaces specific repeat offenders to specific crimes.

Maybe the most well-known example when law implementation utilized data science was after the Boston Marathon bombarding. They used Big Data tech to quickly break down more than 480,000 pictures and separate a few based on codes and algorithms composed based on the descriptions available of the suspect.

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How to build disruptive strategic flywheels

aiuniverse — Tue, 25 Jun 2019 06:42:47 +0000

Source:- strategy-business.com

A large auto manufacturer asked a consulting firm to evaluate its competitive position in relation to ride-sharing startups building autonomous vehicles. Instead of viewing this as a classic strategy project, with a business case, PowerPoint decks, and five-year projections, the firm created a “game” that the automaker could “play” against its competitors. An artificial intelligence (AI) system modeled the voluminous individual choices available to customers, companies, and other entities as digital twins (a digital twin is a computerized replica of a physical asset, process, consumer, actor, or other decision-making entity). The hundreds of thousands of simulations suggested many strategic bets, option-value bets, and “no-regret strategies,” or moves that made strategic and financial sense in a multitude of situations. The selection of those strategies, in turn, made the AI system smarter through learning mechanisms called reinforcement learning, which then further empowered humans to make better decisions. As time progressed, the company was able to choose precise market approaches, pricing, advertising, and customer strategies for multiple cities and communities.

Taken together, these actions created a flywheel, a concept borrowed from the power industry to describe a source of stabilization, energy storage, and momentum, and that was popularized in the strategy context by the author Jim Collins. Executives, instead of trusting instincts and prior assumptions, were able to harness the power of this strategic flywheel to verify hypotheses in simulation and in the real world. Doing so exponentially expanded the array of strategic choices and reduced the cost of experimentation. Rather than paralyzing decision makers with the abundance of options they created, the simulations produced clarifying insights. The result for this auto manufacturer has been a multibillion-dollar valuation of its new services, achieved in less than two years.

Games. AI. Continuous execution and adjustment. Thousands of scenarios to consider. This is not how strategy at blue-chip companies has been done in the past. But it is how business leaders are starting to do strategy now, and how we will need to do strategy in the future — that is, if we are to develop strategies that can both withstand and adapt to the increasing pace of change and disruption that is evident in all industries.

Strategy, the way companies create competitive advantage, has traditionally been a deterministic, linear, and rigid undertaking. The idea is that strategists develop a perfect vision of the future demands of the market, pick a direction or position, invest the full set of resources against it, and execute relentlessly. Strategic planning came into vogue in the late 1960s, and in its pure form was an overarching plan for growth, usually written up in a formal document and endorsed by the CEO. Two decades into the 21st century, this 20th-century tradition continues to be propagated by business schools, by internal planning groups, and by strategy consultants. Even the strategies that seem to work set generic goals and position statements, typically allocate investments on the basis of linear priorities or success metrics (such as return on investment), and create five-year pro forma plans, which are rarely rethought deeply.

But this approach is problematic. The world today is not so deterministic, and the future is highly uncertain. Market and consumer demands, competition, technology, suppliers, and regulations change continually, and the levels and speed of change are intensifying. As a result, the traditional strategic planning process — deterministic, annual, and linear — needs to evolve to become more probabilistic, continual, and multidimensional. In a word, it needs to become more resilient. Organizations can make that shift by adopting a more dynamic approach that leverages AI and advanced analytical techniques. They can then be more sensitive to external market changes, be more rigorous and analytical in evaluating choices and portfolio investments, and make decisions with speed and confidence. In the process, they can develop strategic and growth flywheels that continually reinforce and recalibrate their approach to markets, innovation, and competition.

Clock speed and the flywheel effect

Charles Fine, an MIT professor, introduced the idea of clock speed, the rate at which products or capabilities or business models evolve in different industries. Changes in consumer preferences, technological advances, and regulation are radically accelerating the clock speed of all industries to some degree, and hence the degree of disruption they feel (see the chart in this article).

Capabilities-driven strategy suggests that companies that have a clear way to play (WTP) that aligns with market demands, and that invest in a system of four to six differentiating capabilities that enable the company to excel at the WTP, are better positioned for success. But increasing clock speed changes the calculation. Today, the half-life of a competitive advantage may be fleeting. As industries are disrupted, players that have been successful within the context of one business cycle might need to rethink their differentiating capabilities, their investment portfolios, and possibly even their WTP more frequently and dynamically. Ford no longer just makes cars; it focuses instead on mobility solutions. Big oil companies are investing in renewable energy as a hedge against constraints on emissions. Amazon is competing with…everyone. As a result, it behooves organizations and managers to continually assess competitive moves, regulatory and technology evolution, and consumer preferences — and to adapt decisions in a dynamic fashion.

Using adaptation and experimentation as part of strategy was first suggested by Henry Mintzberg, a professor of management at McGill University. In Mintzberg’s words, companies should “let a thousand strategic flowers bloom…[using] an insightful style, to detect the patterns of success in these gardens of strategic flowers, rather than a cerebral style that favors analytical techniques to develop strategies in a hothouse.” In his book The Fifth Discipline, Peter Senge wrote of the potential to use computer simulations as “growth laboratories.” In fact, many of the most successful companies, including Amazon, Netflix, and Google, experiment scientifically (with test and control groups) and use their growth laboratories to learn from literally hundreds of thousands of experiments in a day. Today, through the use of advanced analytics and AI, the thousand flowers have scaled up exponentially.

Successful disruptors are able to exploit market trends by creating reinforcing feedback loops that give them an advantage over time. Consider the power of data network effects. The more data one has, the more one can personalize the customer experience; the more one personalizes the experience, the more customers are attracted; the more customers one has, the more data one gets. This effect leads consumers to flock disproportionately to a few leaders, thereby creating monopolies or oligopolies.

Let’s look at two remarkably simple examples of companies that have thrived in this age of higher clock speed. Jeff Bezos’s original “napkin” diagram (see “Constructing flywheels”), drawn well before Amazon became a leader in online retailing, describes a virtuous circle of broader product selection, better customer experience, more sellers, more traffic, lower cost structure, and lower prices, all reinforcing one another. A diagram describing Uber’s strategy shows a similar dynamic at work. Faster pickups generate more demand, which attracts more drivers, leading to better geographic coverage, less driver downtime, and lower prices. The components of the flywheels include positions or features that encourage reinforcement through causal effects, thereby increasing exponential and nonlinear adoption. One can assemble such flywheel approaches by thinking carefully about the most important features that are going to drive demand, and the causal linkages between them.

Resilient companies

A well-defined corporate identity, which includes a chosen way to play, associated capabilities, and a portfolio of operations, is helpful in creating boundaries and guidelines for focus. But to thrive through new and disruptive business cycles, companies must continually evolve their capabilities system to match — or even shape — the demands of the market. The secret to more sustained success involves three vital steps. First is adapting by continually sensing market variables, and experimenting with new ideas or bets using a clear mental model for the “spread” (or range of scenarios and possible outcomes) of the decisions being made. Second is developing reinforcing causal feedback loops that provide disproportionate advantage as disruptive market trends take off, and testing, killing, or modifying ideas against this framework constantly. And third is building focus on a WTP and scaling an associated capabilities system, as stated in the capabilities-driven strategy, informed by the needs of the dynamic feedback loop to scale and mature the business model.

Netflix, which our colleagues discussed in their 2017 s+b article on digital disruption, started out competing with Blockbuster Video on the basis of customer convenience and fees. It sent DVDs out by mail and didn’t require return by a specified date, and replaced individual rental fees with a monthly subscription model. In 2007, Netflix disrupted its own business (and killed the rest of Blockbuster’s) by introducing streaming video on demand, which pushed it into competition with cable television. As it amassed customers, Netflix continually refined its analytic capabilities, crunching data to offer more fine-grained recommendations, which consumers could explore more quickly with the flexibility of streaming. This analytic capability fed naturally into the creation of appealing original content, which began with House of Cards in 2013 and expanded to more than 350 original series released in 2017. Bird Box, a Netflix-produced horror film starring Sandra Bullock, was viewed on 45 million accounts in the first seven days after its release in December 2017.

Netflix’s culture and strategy allowed resilience and adaptation all along the way. It gave employees the freedom to explore ideas and learn, was willing to pay at the top of the market for talent, and openly eschewed conventional processes in order to provide room for agility. In effect, Netflix has built three virtuous circles that function as flywheels. There’s a personalization circle: better personalization with AI leading to more customers, more viewing, more data, and, in turn, better personalization. There’s a decision frequency circle: The subscription model leads to more decisions per time unit, which leads to more data and better personalization. And there’s a content creation circle: With more customers, Netflix has more viewings, and hence a better understanding of individual customer preferences, and becomes a more attractive partner for content creators.

Netflix also maintained its identity all along and focused on the capabilities system that enabled the business model to focus and scale within each business cycle. The unlimited subscription model, at the core of the company’s first disruption, was intended to position Netflix to offer streaming when the technology caught up. Once it did, Netflix focused on building world-class capabilities in subscription model administration, online streaming, customer insights, and tailored content production.

Amazon is another great example of a company that has built a resilient strategy by dynamically shaping the market through high-velocity decision making, and creating a flywheel business model. Bezos started Amazon as an online retailer for books, outcompeting traditional brick-and-mortar retailers such as Barnes & Noble, and has since evolved into an online retailer offering anything that can be sold online and shipped.

The focus on technology and logistics helped keep costs low, and enabled Amazon to vastly increase product variety. That, in turn, began to make it the go-to online shopping portal. But Amazon tapped into deeper capabilities to create a flywheel effect with consumers. It mined data to understand consumer preferences, and shaped buying behavior and convenience by introducing such features as one-click ordering and free shipping through Amazon Prime. Over time, the company also expanded into businesses that had nothing to do with retailing: online content streaming; Amazon Web Services, which provides cloud computing services; and new physical products such as Kindle (e-reader), Fire (digital media player), and Echo (smart speaker assistant). Echo’s Alexa has become the technology backbone for interoperability for countless devices exploiting the Internet of Things. This has created another causal loop, as making more devices interoperable on Alexa led to more integration and convenience, leading to higher customer sales, driving more suppliers to integrate with Alexa. Through these many evolutions, Amazon has retained its founding identity of being a customer-driven and technology-led retailer. Bezos wrote in a famous letter to shareholders, “Staying in Day 1 requires you to experiment patiently, accept failures, plant seeds, protect saplings, and double down when you see customer delight.” Amazon’s capabilities system in supply chain and logistics; customer insights and preferences; and online, retail, and technology platform innovation have all been unparalleled.

Peloton, a fitness startup whose US$2,000 stationary bicycles and high-energy classes have gained a cult following since its founding in 2012, started out as a software player, but has since focused directly on every aspect of the value chain, including software, hardware, studio instructors, logistics, and even retail. As CEO John Foley tells the story of its evolution, it is clear that Peloton continued to adapt to market realities and opportunities against conventional thinking, creating a vertically integrated business that controlled all aspects of the customer’s experience. The dynamic feedback loops are evident in the fact that Peloton’s Net Promoter Score (its chief customer satisfaction metric) rose as every new aspect of the experience was controlled. Customer delight with the product and service led to highly efficient word-of-mouth marketing, which led to more customers and greater scale, which improved the company’s financial capacity to build an immersive experience. As it has grown and evolved, Peloton has built out its capabilities systems for software, logistics, and instruction.

Advanced strategy tools

As they work to build growth flywheels, companies can utilize the power of strategy flywheels. In fact, the forces that have led to disruption and accelerated clock speed are also providing businesses with the tools to develop resilient strategy. Automation, analytics, and AI have made huge advances in recent years. In business, we increasingly see machines perform manual or cognitive tasks; organize, analyze, synthesize, and act on large amounts of data; and make operational and management decisions, or at least recommend them. But what about strategy, which is generally regarded as a uniquely human endeavor? To be sure, AI alone can’t develop our strategy for us. But it can change the way we do strategy, and help organizations reimagine the future and develop their own flywheels. In fact, it is already starting to do so.

To be sure, AI alone can’t develop our strategy for us. But it can change the way we do strategy, and help organizations reimagine the future and develop their own flywheels.

It’s helpful to consider the process of strategy formulation, planning, and execution as a game. Games, in general, have a fixed set of rules, are played among a small and known number of players, have well-defined and agreed-upon outcomes, and have known uncertainty (that is, the uncertainty stems from the choices available within the constraints of the game and is not environmentally introduced). In contrast, strategy, or more broadly how businesses operate, is subject to changing rules, is often played against a large and unknown number of players (e.g., disruptors from brand-new industries), has an outcome that is not clear or agreed upon (e.g., maximizing profits or being a socially responsible organization), and is subject to both known and unknown uncertainty (e.g., the emergence of new technology). But the building blocks for games and strategy are actually quite similar, as both involve setting policies, grappling with dynamic models amid the backdrop of environmental assumptions, and coping with randomness.

Looking at strategy through the lens of gaming can lead to a new approach to building dynamic strategic plans that can embed foresight and resilience. As shown in “Sense, think, act” below, the dynamic and resilient flywheel strategy has three components.

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Argo.ai and Carnegie Mellon to found driverless vehicle research center

aiuniverse — Tue, 25 Jun 2019 06:36:27 +0000

Source:- venturebeat.com

Argo.ai, a Pittsburgh, Pennsylvania-based driverless car startup founded by former executives from Google’s and Uber’s autonomous technology divisions, today announced that it’s teaming up with Carnegie Mellon University to form a new center for autonomous vehicle research: the aptly named Carnegie Mellon University Argo AI Center for Autonomous Vehicle Research.

Argo.ai says it’ll pledge $15 million over five years to fund faculty leaders and support graduate students conducting studies in pursuit of their doctorates. Additionally, the company says it’ll provide Carnegie Mellon students engaged in autonomous vehicle research access to data, infrastructure, and platforms like Argoverse, a curated corpus of more than 300,000 vehicle trajectories and 290 kilometers of recorded road lanes.

In a blog post, Argo.ai principal scientist and associate professor at Carnegie Mellon Deva Ramanan said that the Center will investigate smart sensor fusion, 3D scene understanding, urban scene simulation, map-based perception, imitation and reinforcement learning, behavioral prediction, and software validation as they relate to driverless vehicle technology. More broadly, it’ll pursue projects to help self-driving cars overcome hurdles like such as winter weather or construction zones, and Ramanan expects its work will “spur engagements” on topics like safety policy and ethics.

“While the team at [Argo.ai] sees a pathway to achieve initial commercialization opportunities for self-driving technology, there are still advancements required to be able to perceive and navigate autonomously in the most complex, open conditions with dramatically lower compute power,” wrote Ramanan, who added that all of the Center’s findings will be reported in open scientific literature. “And until we’re able to do so at scale, the visionary benefits that have been spelled out for society won’t be achieved.”

Ramanan will serve as the Center’s faculty leader along with Simon Lucey, an associate professor at Carnegie Mellon University’s Robotics Institute specializing in computer vision. The team’s other founding members include John Dolan, David Held, and Jeff Schneider.

“We are thrilled to deepen our partnership with Argo.ai to shape the future of self-driving technologies,” said Carnegie Mellon president Farnam Jahanian. “This investment allows our researchers to continue to lead at the nexus of technology and society, and to solve society’s most pressing problems. Together, Argo.ai and [Carnegie Mellon] will accelerate critical research in autonomous vehicles while building on the momentum of [Carnegie Mellon’s] culture of innovation.”

The Center follows on the heels of Argo.ai’s existing collaboration with Carnegie Mellon and Georgia Tech, but it’s worth noting it’s not the first of its kind. Intel last October announced that it would launch an Institute for Automated Mobility in Arizona, which will combine three state universities; the Departments of Transportation, Public Safety, and Commerce; and companies working on automated cars, trucks, and drones.

Argo has a close relationship with Ford, which in February 2017 said it would invest $1 billion in the startup over the next five years to help it achieve its goal of producing a self-driving vehicle fleet by 2021. This made Ford the company’s largest shareholder and enabled Argo to hire 200 additional employees, many of whom were Ford employees working in the R&D department on a virtual driver system.

Autonomous hardware and software stacks remain Argo’s core projects, along with the high-definition road maps and virtual driver system that will eventually slot into Ford’s self-driving vehicles. Ford has previously said it intends to launch a self-driving taxi and delivery service by 2021.

A.T. Kearney: Get used to competing in digital disorder era

DEAN TAKAHASHI@DEANTAKJUNE 24, 2019 09:01 PM

But the global management consulting firm predicted in a report that by 2030, a new digital era will emerge. The trajectory of the global regulatory environment for technology as well as the extent to which the Internet remains open or balkanized will determine the contours of this period.

To be positioned for the future digital era, businesses must engage in a strategic digital transformation. A.T. Kearney’s Score framework presents a road map for this process.

A.T. Kearney said there are different possible futures, with fears growing about a new “digital cold war” and the “splinternet,” where the Internet becomes more balkanized. This is forcing companies around the world to shift strategies on everything from procurement to customer engagement.

In a new report by A.T. Kearney’s Global Business Policy Council, Competing in an Age of Digital Disorder, the firm said that companies can no longer be passive observers of the digital revolution.

Instead, they must actively adapt to the present disorder while also preparing for the future digital order by embarking on strategic end-to-end digital transformations.

Much attention is focused on the “techlash” nature of new policies on key issues such as consumer privacy, data protection, and anti-competitive practices. But many governments are now seeking to strike a balance in policies that both maximize digital’s upsides and mitigate its downsides as they prepare to regulate the digital environment for the first time. Whether those
governments are able to deftly strike such a balance will influence companies’ ability to use digital technologies effectively in the coming years.

“This cycle of innovation, adoption, and then regulation is consistent with previous waves of technological change,” says Paul Laudicina, chairman of A.T. Kearney’s Global Business Policy Council and co-author of the report, in a statement. “Today, the intense regulatory debate regarding digital technologies is creating a high degree of uncertainty about how the policy environment will evolve.”

After providing a richly researched background on the opportunities and pressure points facing societies, governments, and businesses in this period of digital disorder, the study then offers four scenarios for the digital order that will emerge.

The scenarios are based on two political uncertainties that are unfolding:

Regulatory activity. The extent to which governments in key markets around the world impose new regulations on technology companies and the use of digital technologies more broadly
Digital environment. The extent to which the digital economy is a globalized whole, characterized by extensive cross-border digital flows, or an islandized environment, fragmented into different country-level or regional blocs

“These scenarios are designed to be compelling and plausible visions of the future that challenge and test executives’ capacity to anticipate and plan for their companies’ digital strategies in the coming years,” said Erik Peterson, managing director of the Global Business Policy Council and co-author of the study, in a statement. “In fact, some aspects of these scenarios, such as the
emergence of a digital ‘cold war’ between major global powers and early indications of a ‘splinternet,’ are already playing out in various markets around the world.”

Finally, the study argues that companies cannot be simply spectators of the ongoing digital revolution. Instead, executives will need to guide their organizations through strategic digital transformations across a variety of business functions.

“Companies must adapt to the emerging digital order across strategy, customer experience, operations, risk management and compliance, and employees and culture—our Score framework,” said Courtney Rickert McCaffrey, manager of thought leadership for the Global Business Policy Council and co-author of the study, in a statement. “To compete in the 21st-century digital economy, companies must embark on end-to-end digital transformation in all SCORE

The post Argo.ai and Carnegie Mellon to found driverless vehicle research center appeared first on Artificial Intelligence.

The intelligent shall adopt artificial intelligence early

aiuniverse — Fri, 01 Sep 2017 10:13:21 +0000

Source – livemint.com

British science fiction author Arthur C. Clarke famously wrote in 1962 that “any sufficiently advanced technology is indistinguishable from magic.” In 2017, Artificial Intelligence (AI) seems to be that form of “advanced technology” that is seemingly putting magic into shade.

But what is this magical form of technology? Simply put, AI is based on an algorithm that takes data input in the form of text, speech, video and/or image; processes it through an artificial neural network inspired by the human brain; and generates a decision or an action as the output, which it takes as a learning for future and continues to self-improve, much like an infant. But AI as a technology had been languishing in research labs until a decade ago for want of insane amount of data and computing power it needed to be of any consequence. However, four changes have taken place to spur this revolution in recent times: recent breakthroughs in deep learning; exponential growth in brute processing power and big data; cheaper data storage; and ubiquity of internet-enabled smart mobile devices. It was only last year that Google’s DeepMind AI agent outgunned humans and mastered the infamously difficult Atari game Montezuma’s Revenge, and in 2011 IBM’s Watson famously beat two all-time Jeopardy! champions. But AI is more than becoming video game jocks.

We believe that AI is fast evolving from an obscure curiosity of the last decade to a powerful utility of the current one. There are physical as well as virtual uses, some of which have been in the news ad nauseam while many remain inconspicuous. In the Indian context, corporations can particularly extract a huge business advantage from “Physical-Inconspicuous” and “Virtual-Obvious” applications of AI due to relative ease of implementation.

While that happens, there are three fundamental shifts we all will witness. Firstly, the human-machine interface will drastically change. The web will become less relevant, and the influence of apps will diminish. Customers will instead expect to ask natural questions to get their devices to find data and either present it through a friendly interface or have their request actioned. Secondly, language barriers across the countries and communities will disappear. Dramatic improvements in Natural Language Processing (NLP) will create seamless interaction for culturally and linguistically diverse populations. Recently, Google Translate AI invented its own artificial language to translate language pairs on which it had not been explicitly trained, and this is just the warm-up. And thirdly, the ability to use artificial intelligence and machine learning to enhance decision making or to create autonomous environments will be the key to survival. According to Gartner, by 2019, artificial intelligence platform services will cannibalize revenues for 30% of market-leading companies.

With the buzz around AI, many CEOs today are already keen on harnessing it to their advantage. However, to be able to achieve that, they need to surgically identify where all AI can create the most significant and durable advantage. BCG identified a four-point action framework for executives to help them shape and harness the advantage from AI.

Identify customer needs: Identify the current and potential customers’ explicit and implicit unmet needs.

Adopt technological advances: AI has the ability to improve outcomes and lower cost, so welcome the change in your industry. Invest in holistic AI infrastructure. Explore AIaaS (Artificial Intelligence as a Service) model.

Build and use data sources: Identify, build, and combine existing data with new and even external sources such as databases, optical signals, text, graphs, and speech.

Decompose processes: Break down processes into relatively routinized and isolated elements that can be automated using AI. Then, reassemble them to better meet your customers’ needs.

While there are tremendous advantages, there is a multitude of grey areas that remain to be addressed. For one, AI needs to be regulated at a global level to channelize it in the right direction. Newly founded Partnership on AI is a step in the right direction but legislative measures must also be in place. Also, as the AI systems continue to self-learn and achieve/exceed capabilities of a human brain, we must have humans in the loop in some form in all applications of AI. A framework is needed to give companies, customers and users confidence in the outcomes of self-learning AI systems. For example, why should a doctor/patient trust the AI-based diagnosis and prescription? Finally, the fear of job losses can hinder the pace of AI adoption. Our thinking is that only the easy-to-automate administrative and repetitive tasks in a job will be eliminated, and job profiles will change to focus on things only humans can do. We see jobs getting enriched further, with increased application of human creativity, collaboration, empathy, and judgment. Demand for data scientists will increase, but contrary to general perception, that will be the case only in the short-term. In our view, the demand for their skill is likely to diminish over time as AI systems cross the threshold of completely autonomous self-learning.

These open questions are expected to be addressed as the spectrum of AI disruptions expands. Corporations, however, must start embracing AI early, as it is going to disrupt their industries sooner than one can anticipate.

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