ASAPP founder Gustavo Sapoznik developed software that trains customer-service reps to be radically more productive, winning the young startup an $800 million valuation.

If youve ever felt your blood boil after sitting on hold for 40 minutes before reaching an agent . . . who then puts you back on hold, consider that its often even worse on the other end of the line. A customer-service representative for JetBlue, for instance, might have to flip rapidly among a dozen or more computer programs just to link your frequent-flier number to a specific itinerary.

Imagine that cognitive load, while you have someone screaming at you or complaining about some serious problem, and youre swiveling between 20 screens to see which one you need to be able to help this person, says Gustavo Sapoznik, 34, the founder and CEO of ASAPP, a New York Citybased developer of AI-powered customer-service software.

Sapoznik remembers just such a scene while shadowing a call-center agent at a very large company (he wont name names), watching the worker navigate a Frankenstack patchwork of software, entering a callers information into six different billing systems before locating it. That was an eye-opening moment.

The problem has only gotten worse during the pandemic. Call centers for banks, finance companies, airlines and service companies are being overrun. Call volumes for ASAPPs customers have spiked between 200% and 900% since the crisis began, according to Sapoznik. Making call centers work isnt the sexiest use of cutting-edge AI, but its a lucrative one.

If we can automate half of this thing away, we can get to the same place by making people twice as productive.

According to estimates from Forrester Research, global revenues for call centers are around $15 billion a year. In all, ASAPP has raised $260 million at a recent valuation of $800 million, per data from Pitchbook. Silicon Valley heavy hitters including Kleiner Perkins chairman John Doerr and former Cisco CEO John Chambers are on ASAPPs board, along with Dave Strohm of Greylock and March Capitals Jamie Montgomery. Clients include JetBlue, Sprint and satellite TV provider Dish, all of whom who sign up for multiyear contracts contributing to ASAPPs estimated $40 million in revenue, according to startup tracker Growjo.

ASAPP has drawn this investor interest by flipping AI on its head. For years engineers have perfected artificial intelligence to perform repetitive tasks better than humans. Rather than having people train AI systems to replace them, ASAPP makes AI that trains people to be radically more productive.

Pure automation capabilities are [used] out of an imperative to reflect costs, but at the expense of customer experience. Theyve been around for 20 or 30 years but they havent really solved much of the problem, Sapoznik says. ASAPPs thinking: If we can automate half of this thing away, we can get to the same place by making people twice as productive.

The company is a standout on Forbes second annual AI 50 list of up-and-coming companies to watch, rated highly for its use of artificial intelligence as a core attribute by an expert panel of judges. Its focus on using AI to keep humans in the loop is also what sets ASAPP apart, although its competing in the same call-center sandbox as fellow AI 50 listees Observe.ai of San Francisco and Cresta, which is chaired by AI legend Sebastian Thrun, the Stanford professor who greenlit Googles self-driving car program.

ASAPPs focus is natural language processing and converting speech to text using proprietary technology developed by a group led by a found- ing member of the speech team for Apples Siri. Its software then displays suggested responses or relevant resources on a call-center agents screen, minimizing the need to toggle between applications. Sapoznik and his engineers also studied the most effective human representatives, trying to replicate their expertise into ASAPP software via machine learning. That software then coaches call-center staff on effective ways to respond to customer queries and tracks down critical information. If a caller asks how to cancel a flight, for example, ASAPP software automatically pulls up helpful documents for the agent to browse. If a customer reads a 16-digit account number, its instantly transcribed and displayed on the agents screen for easy reference.

When things go right, companies using ASAPP technology see the number of calls successfully handled per hour increase from 40% to more than 150%. That can mean lower stress for call- center workers, which in turn reduces the high turnover associated with that line of work.

A licensed pilot with a fondness for classical music who studied math at the University of Chicago, Sapoznik first applied his coding skills to his familys real estate and financial business in Miami. Id been doing some work in investments where you build machine-learning product capabilities to trade the markets. The impact there is that theres a number that goes up or goes down, he says. Merely making money didnt excite him.

Sapoznik hopes that optimizing call centers is just a start for ASAPP, which he founded in 2014. Hes actively searching for similar gigantic-size business opportunities with brokenness and tons of interesting data. He thinks ASAPP can do that because its built like a research organization80% of its 300 employees are researchers or engineers.

The exciting thing about ASAPP is not so much what theyre going after now, but whether or not they can go beyond that, says Forrester analyst Kjell Carlsson. They, like so many of us, see the incredible potential of [using] natural language processing for augmented intelligence.

Summarizing ASAPPs potential, Sapoznik draws on his experience as a pilotin aviation, automation has steadily transformed the cock- pit. Its increased safety from a pretty dramatic perspective, and it hasnt gotten rid of pilots yet, he says. Its just taken away chunks of their workloads.

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Meet The AI Designed To Help Humans, Not Replace Them - Forbes

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Can a robot painter learn from observing a human artists brushstrokes? Thats the question Carnegie Mellon University researchers set out to answer in a study recently published on the preprint server Arxiv.org. They report that 71% of people found the approach the paper proposes successfully captured characteristics of an original artists style, including hand-brush motions, and that only 40% of that same group could discern the brushstrokes drawn by the robot.

AI art generation has been exhaustively explored. An annual international competition RobotArt tasks contestants with designing artistically inclined AI systems. Researchers at the University of Maryland and Adobe Research describe an algorithm called LPaintB that can reproduce hand-painted canvases in the style of Leonardo da Vinci, Vincent van Gogh, and Johannes Vermeer. Nvidias GauGAN enables an artist to lay out a primitive sketch thats instantly transformed into a photorealistic landscape via a generative adversarial AI system. And artists including Cynthia Hua have tapped Googles DeepDream to generate surrealist artwork.

But the Carnegie Mellon researchers sought to develop a style learner model by focusing on the techniques of brushstrokes as intrinsic elements of artistic styles. Our primary contribution is to develop a method to generate brushstrokes that mimic an artists style, they wrote. These brushstrokes can be combined with a stroke-based renderer to form a stylizing method for robotic painting processes.

The teams system comprises a robotic arm, a renderer that converts images into strokes, and a generative model to synthesize the brushstrokes based on inputs from an artist. The arm holds a brush that it dips into buckets containing paints and puts the brush to canvas, cleaning off the extra paint between strokes. The renderer uses reinforcement learning to learn to generate a set of strokes based on the canvas and a given image, while the generative model identifies the patterns of an artists brushstrokes and creates new ones accordingly.

To train the renderer and generative models, the researchers designed and 3D-printed a brush fixture equipped with reflective markers that could be tracked by a motion capture system. An artist used it to create 730 strokes of different lengths, thicknesses, and forms on paper, which were indexed in grid-like sheets and paired with motion capture data.

In an experiment, the researchers had their robot paint an image of the fictional reporter Misun Lean. They then tasked 112 respondents unaware of the images authorship 54 from Amazon Mechanical Turk and 58 students at three universities to determine whether a robot or a human painted it. According to the results, more than half of the participants couldnt distinguish the robotic painting from an abstract painting by a human.

In the next stage of their research, the team plans to improve the generative model by developing a stylizer model that directly generates brushstrokes in the style of artists. They also plan to design a pipeline to paint stylized brushstrokes using the robot and enrich the learning dataset with the new samples. We aim to investigate a potential artists input vanishing phenomena, the coauthors wrote. If we keep feeding the system with generated motions without mixing them with the original human-generated motions, there would be a point that the human-style would vanish on behalf of a new generated-style. In a cascade of surrogacies, the influence of human agents vanishes gradually, and the affordances of machines may play a more influential role. Under this condition, we are interested in investigating to what extent the human agents authorship remains in the process.

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Carnegie Mellons robotic painter is a step toward AI that can learn art techniques by watching people - VentureBeat

A People's Liberation Army Navy fleet including the aircraft carrier Liaoning, submarines, vessels and fighter jets takes part in a review in the South China Sea on Apr. 12, 2018.

Visual China Group | Getty Images

The United States recently sent two aircraft carrier strike groups into the South China Sea in a show of military strength. The move of multiple American warships is in reaction to China holding military exercises in international waters that are contested by Vietnam and the Philippines. The stand-off raises global tensions at a time when each superpower has developed advanced technological capabilities in terms of artificial intelligence, remote imaging, and autonomous weapons systems. It is important officials in each nation understand how emerging technologies speed up decision-making but through crisis acceleration run the risk of dangerous miscalculation.

Harkening back to Prussian general and military theorist Carl von Clausewitz's famous work"On War," military doctrine the world over has been rooted in an understanding of the ever-changing character of war, the ways in which war manifests in the real world, and the never-changing nature of war, those abstractions that differentiate war from other acts namely its violent, political, and interactive elements. Military scholars and decision makers alike have discussed and debated these definitions time and time again, with the character of war often being defined by the technologies of the day, and the nature of war being articulated as the human element of armed conflict.

With the advent of AI and other emerging technologies, though, these time-honored definitions are likely to change. At a fundamental level, battle, war, and conflict are time-competitive processes. From time immemorial, humans have sought to be faster in the ultimate competition of combat, in an absolute as well as in a relative sense. And in that regard, AI will dramatically change the speed of war. It will not only enhance the human role in conflict, but will also leverage technology as never before. For not only is technology changing, the rate of that alteration is accelerating.

This is the central issue before us for armed conflict, and the side that can create, master, and leverage an equilibrium between the nature of war and the character of war, especially within the new environment of AI, data analytics, and supercomputing, will inevitably prevail in conflict. In a geopolitical environment increasingly defined by new and emerging technologies, national defense stands as one of the most consequential areas of development for the 21st century. It is important to assess the revolutionary impacts of artificial intelligence and other emerging technologies on nearly every facet of national security and armed conflict, including the accelerated pace of warfare and the critical role of continued human control.

AI, once fully realized, has the potential to be one of the single greatest force multiplier for military and security forces in human history.

Ultimately, there are significant opportunities to deploy AI-based tools, as well as major rising threats that need to be considered and addressed. A variety of technologies can improve decision-making, speed, and scalability some to a dizzying degree. But, as with so many other AI applications, policy and operational shifts are necessary to facilitate the proper integration and innovation of these emerging technologies and make sure they strengthen, not weaken, leadership capacity, general readiness, and performance in the field.

Throughout human history, militaries have operated as the most overt political tool available to governments and society. Clausewitz himself famously wrote, "War is a continuation of politics by other means." And while modern security forces play a variety of interchangeable roles (peacekeeping, stabilization, and national defense), they invariably represent the threat of violence the "mailed fist" purpose-built to ensure a particular outcome.

With this as context, it is no surprise that the ability to assure outcomes and plan for all contingencies, violent or otherwise, takes up a significant portion of military leadership and military strategists' time and energy. Here, through anything from predictive analytics to lightning-fast target acquisition, AI, once fully realized, has the potential to be one of the single greatest force multiplier for military and security forces in human history.

Indeed, as noted in a Congressional Research Service report: "AI has the potential to impart a number of advantages in the military context, [though] it may also introduce distinct challenges. AI technology could, for example, facilitate autonomous operations, lead to faster, more informed military decision-making, and increase the speed and scale of military action. However, it may also be unpredictable or vulnerable to unique forms of manipulations."

Rather than constituting a single weapon system itself, AI is instead being built into a wide variety of weapons systems and core infrastructure. Tanks, artillery, aircraft, submarines versions of each can already detect objects and targets on their own and maneuver accordingly.

Alan Radecki | U.S. Navy | Northrop Grumman | Getty Images

The interim report of the U.S. National Security Commission on Artificial Intelligence warns that:

"How the United States adopts AI will have profound ramifications for our immediate security, economic well-being, and position in the world. Developments in AI cannot be separated from the emerging strategic competition with China and developments in the broader geopolitical landscape. We are concerned that America's role as the world's leading innovator is threatened. We are concerned that strategic competitors and non-state actors will employ AI to threaten Americans, our allies, and our values. We know strategic competitors are investing in research and application. It is only reasonable to conclude that AI-enabled capabilities could be used to threaten our critical infrastructure, amplify disinformation, and wage war."

AI's role in the military and on the battlefield is thus one of catalytic power, both for good and ill. Yet, the strength of AI does not manifest in the way a bomb or new weapons platform might perform or act. Its utility is much broader. As noted by Brookings Institution scholar Chris Meserole, AI is being deployed in myriad ways by the American military: "Rather than constituting a single weapon system itself, AI is instead being built into a wide variety of weapons systems and core infrastructure. Tanks, artillery, aircraft, submarinesversions of each can already detect objects and targets on their own and maneuver accordingly."

This dynamic becomes particularly clear within the context of the spectrum of conflict modern militaries deal with today, notably hybrid conflict. Looking ahead, it will also define warfare of the future, namely through what John Allen and Amir Husain have coined as "hyperwar." The distinctions between hybrid warfare and hyperwar are important. As noted in a recent NATO report: "Hybrid threats combine military and non-military as well as covert and overt means, including disinformation, cyber attacks, economic pressure, deployment of irregular armed groups and use of regular forces. Hybrid methods are used to blur the lines between war and peace, and attempt to sow doubt in the minds of target populations."

Russian servicemen operate an S-400 Triumf anti-aircraft missile system on combat duty in the Kaliningrad Region in 2019. The system is designed to repel any contemporary aerospace attack, such as stealth and fighter aircraft, bombers, cruise and ballistic missiles, drones and hypersonic targets.

Vitaly Nevar | TASS | Getty Images

In this environment, AI can super-charge an adversary's ability to sow chaos in the battlespace and incorporate deception and surprise into their tactics in new and novel ways. By contrast, hyperwar may be defined as a type of conflict where human decision-making is almost entirely absent from the observe-orient-decide-act (OODA) loop, a popular framework developed by U.S. Air Force Colonel John Boyd for training individuals to make time-sensitive decisions as quickly as possible, especially when there is limited time to gather information. As a consequence, the time associated with an OODA cycle will be reduced to near-instantaneous responses.

The implications of these developments are many and game changing, ranging across the spectrum from advanced technological modernization to how leaders in the era of hyperwar are recruited, educated and trained. The topics of speed, efficiency, and accuracy as well as the necessity for human control of AI-powered military capabilities represent the heart of the issue in the current AI-national security debate.

With U.S. and Chinese forces maneuvering in the relatively tight operational environment of the South China Sea, reaction times are already short. As more sophisticated AI eventually enables faster and more comprehensive intelligence collection and analysis, rapid decision support, and even wide area target acquisition, we could see a premium being placed on "going first" and not risk being caught flat-footed. While AI has the capacity to magnify military capabilities and accelerate the speed of conflict, it can also be inherently destabilizing.

Now is the time for the U.S. and China to have the hard conversations about norms of behavior in an AI enabled, hyperwar environment. With both sides moving rapidly to field arsenals of hypersonic weapons, action and reaction times will become shorter and shorter and the growing imbalance of the character and nature of war will create strong incentives, in moments of intense crisis, for conflict not peace. This is foreseeable now, and demands the engagement of both powers to understand, seek, and preserve the equilibrium that can prevent the sort of miscalculation and high-speed escalation to the catastrophe that none of us wants.

ByJohn R. Allen, a retired four-star military general and Brookings Institution president, and Darrell West, vice president of governance studies at Brookings. This article is excerpted from their forthcomingTurning Point: Policymaking in the Era of Artificial Intelligence.

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Op-ed: Hyperwar is coming. America needs to bring AI into the fight to win with caution - CNBC

NEW YORK, July 14, 2020 /PRNewswire/ -- In times of uncertainty, fear, anxiety, and stress are natural responses to real or perceived threats. Faced with the new realties, of today's current environment, an idea was born. SARAH the AI Therapist, where users can engage 24/7 to seek comfort, advice and inspire confidence by positively influencing their lives, particularly when suffering from psychological and mental health issues. Unlike chat bots, SARAH'sartificial intelligence allows it to become personal with its users, improving its responsiveness as time progresses. Unique features include the ability to recall previous conversations, detect emotions by sound vibration and calm users down by providing solutions. By listening and understanding SARAH can provide counseling therapy and optimize users' schedules through a reminder and track feature.

SARAH can help individuals, couples, and families within multiple fields of Therapy, from depression to PTSD and from couples counseling to children's psychology. Moreover, SARAH can advise its users on the ways they can improve their relationship with themselves as well as pointers on how they can finally rid themselves of their negative habits and replace them with positive ones.

Mental health is a serious issue and with ever-increasing pressure on our daily lives symptoms and signs ofdepression, anxiety, and fear are not going away. Since the beginning of the COVID-19 pandemic and isolation efforts in March, people everywhere have been reporting increasing pressure on their mental health. Whether it is because they are now out of employment or fear losing their job, people are struggling to piece together money to pay bills. The continuous onslaught of worry and insecurity is leading to greater psychological issues than ever before.

There is so much SARAH can contribute and Founder, Alireza Dehghanis very excited about this. "This is an opportunity for all of us to make a difference by joining a cause and contributing back to society."

SARAH has started a crowdfundingcampaign on Indiegogoand funds will be used to advance Dehghan's dream of providing affordable on-demand therapy to all those in need."In an ideal world, everyone should have easy, affordable access to mental health care professionals and be able to properly treat their maladies" states Dehghan.

Subscriptions are available at a 65% discount whereby sponsors can help by donating a subscription to a mentally ill person or families struggling with the repercussions of COVID-19.

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Alireza DehghanPhone Number: +1 (917)-936-2030Email: [emailprotected]

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World's First AI Therapist, SARAH, is Born. - PRNewswire

July 14, 2020Lunit, a leading medical AI startup, announced the expansion of its collaboration with GE Healthcare. This collaboration between GE Healthcare and Lunit will help make AI algorithms more accessible to clinicians, alleviate clinical strain and streamline workflows supporting better patient outcomes.

GE Healthcare recently introduced itsThoracic Care Suitefeaturing a collection of eight artificial intelligence (AI) algorithms fromLunit INSIGHT CXR.The AI Suite quickly analyzes chest x-ray findings and flags abnormalities to radiologists for review, including pneumonia, which may be indicative of COVID-19 as well as tuberculosis, lung nodulesand other radiological findings.

This collaboration between GE Healthcare and Lunit is one of the first of its kind to bring commercially available AI products from a medical AI startup to an existing X-ray equipment manufacturer making Lunit INSIGHT CXR available via Thoracic Care Suite to GE Healthcares thousands of global fixed, mobile, and R&F X-ray customers at point of sale.

As a startup company, our vision is to have AI to be recognized as the new standard of care, said Brandon Suh, CEO of Lunit. We have been applying our AI into various types of medical images. Among them, Lunit INSIGHT CXR is one of our major products that has been commercialized since a few years ago. To have our AI made available with a market-leading vendor like GE Healthcare especially as part of the Thoracic Care Suite is a significant advancement in delivering solutions to various customers within GE Healthcares install base and bringing us all one step closer to embracing AI as a part of todays standard of care. We will continue to push forward and cooperate with market leaders through extended partnerships and collaborations, increasing the number of global use cases of our AI.

Lunits original algorithm, Lunit INSIGHT CXR, is designed to provide accurate and instant analysis of chest x-ray images by mapping the location of the findings and displaying the scored calculation of its actual existence. In a COVID-19 setting, Lunit INSIGHT CXR can be useful in quickly identifying high-risk cases as well as monitoring patients progression and regression of mild respiratory symptoms. The algorithm performs at 97-99% accuracy rate (Area Under the Curve - AUC), and according to studies published in Radiologyand JAMA Network Open, a physician can see their performance increase up to 20% upon interpreting with the assistance of Lunits AI.

Like Lunit, GE Healthcare is committed to developing and providing AI solutions to help alleviate our customers most pressing challenges, says Katelyn Nye, General Manager of GE Healthcare mobile X-ray and AI products. Weve watched Lunits success closely over the past several years and are excited to marry our expertise as a well-established x-ray manufacturer with the nimbleness of a startup like Lunit. We believe that by featuring Lunit INSIGHT CXR as a part of our Thoracic Care Suite we can meet our customers most pressing needs and expand AI adoption in healthcare.

Lunit is also actively working with GE Healthcare to integrate its AI solutions into GEsEdison Open AI Orchestrator, an algorithm management solution to help physicians and healthcare organizations work more efficiently and effectively. Available via a standalone server or integrated into GE Healthcares Centricity Picture Archiving and Communication System (PACS) and Enterprise Imaging System (EIS), these algorithms can help clinicians manage chest radiography reading more efficiently, improve clinicians workflow and provide a more native reading experience. Once complete, this offering will help reduce the complexity of implementing and managing multiple systems and algorithms as well as provide an easy way for organizations to adopt and explore AI.

Additionally, Lunit is an innovator under GE Healthcares Edison Developer Program, which helps healthcare providers gain easier access to market-ready algorithms and applications such as Lunit INSIGHT CXR by directly integrating these technologies into existing workflows. As such, Lunit and GE Healthcare are actively working to deliver novel and targeted deep-learning technology for healthcare systems via Edison, GE Healthcares secure intelligence platform.

For more information: http://www.lunit.io

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Lunit Expands Collaboration with GE Healthcare to Advance AI Adoption - Imaging Technology News

Team members at Adobe have built a new way to use artificial intelligence to automatically personalize a blog for different visitors.

This tool was built as part of the Adobe Sneaks program, where employees can create demos to show off new ideas, which are then showcased (virtually, this year) at the Adobe Summit. While the Sneaks start out as demos, Adobe Experience Cloud Senior Director Steve Hammond told me that 60% of Sneaks make it into a live product.

Hyman Chung, a senior product manager for Adobe Experience Cloud, said that this Sneak was designed for content creators and content marketers who are probably seeing more traffic during the coronavirus pandemic (Adobe says that in April, its own blog saw a 30% month-over-month increase), and who may be looking for ways to increase reader engagement while doing less work.

So in the demo, the Experience Cloud can go beyond simple A/B testing and personalization, leveraging the companys AI technology Adobe Sensei to suggest different headlines, images (which can come from a publishers media library or Adobe Stock) and preview blurbs for different audiences.

Image Credits: Adobe

For example, Chung showed me a mocked-up blog for a tourism company, where a single post about traveling to Australia could be presented differently to thrill-seekers, frugal travelers, partygoers and others. Human writers and editors can still edit the previews for each audience segment, and they can also consult a Snippet Quality Score to see the details behind Senseis recommendation.

Hammond said the demo illustrates Adobes general approach to AI, which is more about applying automation to specific use cases rather than trying to build a broad platform. He also noted that the AI isnt changing the content itself just the way the content is promoted on the main site.

This is leveraging the creativity youve got and matching it with content, he said. You can streamline and adapt the content to different audiences without changing the content itself.

From a privacy perspective, Hammond noted that these audience personas are usually based on information that visitors have opted to share with a brand or website.

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Adobe tests an AI recommendation tool for headlines and images - TechCrunch

SEOUL, South Korea, July 14, 2020 /PRNewswire/ -- VUNO Inc., South Korean artificial intelligence (AI) developerannounced that it will attend the European Congress of Radiology 2020 (ECR 2020)to be held fromJuly 15 to July 19, 2020 to showcase the power of its flagship AI radiology solutions that have recently received the CE mark.As part of its ambitious plan to go global, VUNO is set to seize this opportunity to further expand its networks with sales prospects and business partners from all around the world.

ECR, one of the leading events in the field of radiology, will be joined by industry experts, medical & healthcare professionals, modality manufacturers and solutions developers, etc. In the light of the COVID 19 epidemic, the congress organizers have decided to opt for an online-only event. With about 2,100 leading industry representatives onboard, the exhibition of ECR 2020 can be accessed from 08:00 a.m. July 15 to 21, until 11:55 p.m. CEST. Free registration & participation is available on the ECR 2020 Virtual Exhibition website. (https://ecr2020.expo-ip.com/).

VUNO's exhibition at the event will include VUNO MedLungCT AI that detects, locates, and quantifies pulmonary lung nodules on CT images, VUNO Med-Chest X-Ray that assists in the chest X-ray readings of common thoracic abnormalities on chest radiographs. VUNO Med-DeepBrain is a diagnostic supporting tool for degenerative brain diseases through brain parcellation & quantification on brain MR images.

On top of the three solutions to be showcased at this event, VUNO has two other solutions that have gained CE certifications recently- VUNO Med-BoneAge and VUNO Med Fundus AI. All these five products are now available to be marketed in countries where CE marking is acceptable.

VUNO Med solutions are designed to be agnostic to any devices and any environments offering seamless integration with any PACS and/or EMR systems. They are offered via cloud servers allowing users to analyze images anytime, anywhere with access to the Internet. They are also available through on-premise installations as well.

VUNO has the highest number of clients with more than120 medical institutions in Korea alone. With its huge successes under its belt rooted in the proven effectiveness and safety through clinical trials and practices, the company is embarking on a new endeavor to proclaim its technical prowess in overseas markets now by signing partnerships with global healthcare giants like M3, a SONY subsidiary and Japan's largest data platform company.

For more detailed information on VUNO, visit https://www.vuno.co/.

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Discover the Power of VUNO's AI Solutions at ECR 2020 - WFMZ Allentown

]]> AI.Reverie has secured SBIR Phase 2 contract from AFWERX. Credit: Markus Spiske on Unsplash.

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AI.Reverie has secured Phase 2 Small Business Innovation Research (SBIR) contract from AFWERX for the US Air Force (USAF).

Under the $1.5m contract, AI.Reverie will build artificial intelligence (AI) algorithms and improve navigation capabilities supporting the 7th Bomb Wing at Dyess Air Force Base (AFB).

The company will use synthetic data to train and improve the accuracy of vision algorithms for navigation through its Rapid Capabilities office.

Synthetic data or computer-generated images are economical and can be generated faster than hand-labelled photos, solving the limitations associated with real data.

The advanced technology creates vision algorithms needed to save lives during operations.

Phase 2 SBIR contract awarded to AI.Reverie follows its co-publication with the IQT Lab CosmiQ Works that highlighted the value of synthetic data to train computer vision algorithms.

Furthermore, the research partners released RarePlanes for academic and commercial use with open dataset of real and synthetic overhead imagery.

USAF Major Anthony Bunker said: As the world has gotten smaller, the ability to navigate based on visual terrain features has become an ever-increasing challenge.

Computer vision algorithms can be trained to recognise these world-wide terrain features by ingesting large amounts of diverse data.

We are excited to collaborate with AI.Reverie to improve navigation capabilities given the companys ability to generate fully annotated data at scale with its synthetic data platform.

In May this year, AI.Reverie and Green Revolution Cooling (GRC) secured AFWERX SBIR Phase I contract from the USAF. The contract was for enhancing computer vision models for the US Department of Defense (DoD).

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AI.Reverie wins contract to improve navigation capabilities for USAF - Airforce Technology

New Delhi | Updated: July 13, 2020 4:33:10 pm

Implementing Artificial Intelligence (AI) in an organization is a complex undertaking as it involves bringing together multiple stakeholders and different capabilities. Many companies make the mistake of treating AI as a pure play technology implementation project and hence end up encountering many challenges and complexities peculiar to AI. There are three big reasons for increased complexity in an AI program implementation (1) AI is a portfolio based technology (example, comprising sub-categories such as Natural Language Processing (NLP), Natural Language Generation (NLG), Machine Learning) as compared to many standalone technology solutions (2) These sub-category technologies (example, NLP) in turn have many different products and tool vendors with their own unique strengths and maturity cycles (3) These sub-category technologies (example, NLG) are specialists in their functionality and can solve certain specific problems only (example, NLG technology helps create written texts similar to how a human would create it). Hence, organizations need to do three important things Define Ambitious and Achievable Success Criteria, Develop the Right Operating Rhythm, and Create and Celebrate Success Stories to realize the true potential of AI.

Most companies have very narrow or ambiguous success criteria definition of their AI program. These success criteria are not defined holistically and hence may end up providing sub-optimal benefits to the organization. We suggest that the success criteria of an AI program need to not only be ambitious, achievable, and actionable but also tightly integrated with the overall key strategic objectives and priorities of the organization. For example, a bank which is trying to reduce the number of customer complaints and improve the customer experience as key strategic goals can benefit immensely from integrating AI program goals with the goals of this important program (example, leverage machine learning and analytics to analyze past complaints data and better understand customer complaints patterns and journeys and decision points). This interlocking of success criteria will help AI program leaders with the right yardsticks to align and measure their progress and contribution. Additionally, it also helps them get the right visibility and sponsorship at the senior leadership levels in the organization that further improves the chances of success of the AI program.

Also Read: Can Humans and AI coexist to create a hyper-productive HumBot organisation?

A successful AI program requires four key ingredients Right Data, Diverse Skills, Scalable Infrastructure and Seamless Stakeholder Alignment. It is said that Data is the food of an AI program and hence having the right data (example, the volume of data, type of data, and quality of data) at the right time is critical to ensure AI programs have the required fuel and energy to complete their intended journey. While good AI skills are in short supply, leveraging constructs such as having a Nimble CoE (Centers of Expertise) increases chances of optimal utilization of these rare and expensive skills across the organization. Finally, getting various important stakeholders (example, Global Process Owners, IT Leaders, Internal Control & Risks, Continuous Improvement, and HR) seamlessly work together is important to reduce friction and increase AI program velocity.

Also Read:With the power of AI, India can reimagine delivery of public services

It is said that success breeds more success. While AI programs typically focus a lot on efficiency and productivity improvements, many AI programs also generate significant non-direct-quantifiable benefits (for example, improvement in stakeholder experience, improvement in employee engagement and morale). A recent Deloitte survey indicates that 44 per cent organizations felt AI has increased the quality of their products/services while 35 per cent organizations found that AI has enabled them to make better decisions in their organizations. Successful companies find a way to identify these simple, holistic stories and narrate them compellingly and consistently in multiple forums at all levels in their organizations. Humans, by design, are inspired better by stories (than by just numbers) and hence creating a powerful story that combines the quantifiable (example, number of hours saved) with other benefits (example, better decision making ability) can galvanize the entire organization and facilitate rapid and increased adoption of AI at all levels and in all units of the organization.

The revered Chinese saint, Lao Tzu, once famously remarked that A journey of thousand miles begins with a single step. The AI journey in an organization is no exception. While AI implementations are typically more complex and nuanced, companies can leverage the 3-pronged approach mentioned above to realize the true and full potential of AI. While a successful AI program implementation can bestow significant financial benefits on an organization, it also activates the divine journey of freeing up humans to do what they do best leverage their sophisticated brains to introspect, explore, learn, love, empathize and solve the most intricate and defining problems of our generations.

The authors are Ravi Mehta, Partner; Sushant Kumaraswamy, Director; Sudhi. H, Associate Director; and Prashant Kumar, Senior Consultant, Deloitte India.

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The journey that organizations should embark on to realize the true potential of AI - The Indian Express