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Virtual dressing room startup Revery.ai applying computer vision to the fashion industry – TechCrunch

August 26, 2021 | Author: admin

Figuring out size and cut of clothes through a website can suck the fun out of shopping online, but Revery.ai is developing a tool that leverages computer vision and artificial intelligence to create a better online dressing room experience.

Under the tutelage of University of Illinois Center for Computer Science advisrr David Forsyth, a team consisting of Ph.D. students Kedan Li, Jeffrey Zhang and Min Jin Chong, is creating what they consider to be the first tool using existing catalog images to process at a scale of over a million garments weekly, something previous versions of virtual dressing rooms had difficulty doing, Li told TechCrunch.

Revery.ai co-founders Jeffrey Zhang, Min Jin Chong and Kedan Li. Image Credits: Revery.ai

California-based Revery is part of Y Combinators summer 2021 cohort gearing up to complete the program later this month. YC has backed the company with $125,000. Li said the company already has a two-year runway, but wants to raise a $1.5 million seed round to help it grow faster and appear more mature to large retailers.

Before Revery, Li was working on another startup in the personalized email space, but was challenged in making it work due to free versions of already large legacy players. While looking around for areas where there would be less monopoly and more ability to monetize technology, he became interested in fashion. He worked with a different adviser to get a wardrobe collection going, but that idea fizzled out.

The team found its stride working with Forsyth and making several iterations on the technology in order to target business-to-business customers, who already had the images on their websites and the users, but wanted the computer vision aspect.

Unlike its competitors that use 3D modeling or take an image and manually clean it up to superimpose on a model, Revery is using deep learning and computer vision so that the clothing drapes better and users can also customize their clothing model to look more like them using skin tone, hair styles and poses. It is also fully automated, can work with millions of SKUs and be up and running with a customer in a matter of weeks.

Its virtual dressing room product is now live on many fashion e-commerce platforms, including Zalora-Global Fashion Group, one of the largest fashion companies in Southeast Asia, Li said.

Revery.ai landing page. Image Credits: Revery.ai

Its amazing how good of results we are getting, he added. Customers are reporting strong conversion rates, something like three to five times, which they had never seen before. We released an A/B test for Zalora and saw a 380% increase. We are super excited to move forward and deploy our technology on all of their platforms.

This technology comes at a time when online shopping jumped last year as a result of the pandemic. Just in the U.S., the e-commerce fashion industry made up 29.5% of fashion retail sales in 2020, and the markets value is expected to reach $100 billion this year.

Revery is already in talks with over 40 retailers that are putting this on their roadmap to win in the online race, Li said.

Over the next year, the company is focusing on getting more adoption and going live with more clients. To differentiate itself from competitors continuing to come online, Li wants to invest body type capabilities, something retailers are asking for. This type of technology is challenging, he said, due to there not being much in the way of diversified body shape models available.

He expects the company will have to collect proprietary data itself so that Revery can offer the ability for users to create their own avatar so that they can see how the clothes look.

We might actually be seeing the beginning of the tide and have the right product to serve the need, he added.

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Virtual dressing room startup Revery.ai applying computer vision to the fashion industry - TechCrunch

Man Robbed of 16 Bitcoin Sues Young Thieves’ Parents Krebs on Security – Krebs on Security

August 26, 2021 | Author: admin

In 2018, Andrew Schober was digitally mugged for approximately $1 million worth of bitcoin. After several years of working with investigators, Schober says hes confident he has located two young men in the United Kingdom responsible for using a clever piece of digital clipboard-stealing malware that let them siphon his crypto holdings. Schober is now suing each of their parents in a civil case that seeks to extract what their children would not return voluntarily.

In a lawsuit filed in Colorado, Schober said the sudden disappearance of his funds in January 2018 prompted him to spend more than $10,000 hiring experts in the field of tracing cryptocurrency transactions. After months of sleuthing, his investigators identified the likely culprits: Two young men in Britain who were both minors at the time of the crime (both are currently studying computer science at U.K. universities).

A forensic investigation of Schobers computer found hed inadvertently downloaded malicious software after clicking a link posted on Reddit for a purported cryptocurrency wallet application called Electrum Atom. Investigators determined that the malware was bundled with the benign program, and was designed to lie in wait for users to copy a cryptocurrency address to their computers temporary clipboard.

When Schober went to move approximately 16.4 bitcoins from one account to another by pasting the lengthy payment address hed just copied the malware replaced his bitcoin payment address with a different address controlled by the young men.

Schobers lawsuit lays out how his investigators traced the stolen funds through cryptocurrency exchanges and on to the two youths in the United Kingdom. In addition, they found one of the defendants just hours after Schobers bitcoin was stolen had posted a message to GitHub asking for help accessing the private key corresponding to the public key of the bitcoin address used by the clipboard-stealing malware.

Investigators found the other defendant had the malware code that was bundled with the Electrum Atom application in his Github code library.

Initially, Schober hoped that the parents of the thieving teens would listen to reason, and simply return the money. So he wrote a letter to the parents of both boys:

It seems your son has been using malware to steal money from people online, reads the opening paragraph of the letter Schober emailed to the families. Losing that money has been financially and emotionally devastating. He might have thought he was playing a harmless joke, but it has had serious consequences for my life.

A portion of the letter than Schober sent to two of the defendants in 2018, after investigators determined their sons were responsible for stealing nearly $1 million in cryptocurrency from Schober.

Met with continued silence from the parents for many months, Schober filed suit against the kids and their parents in a Colorado court. A copy of the May 2021 complaint is here (PDF).

Now they are responding. One of the defendants Hazel D. Wells just filed a motion with the court to represent herself and her son in lieu of hiring an attorney. In a filing on Aug. 9, Wells helpfully included the letter in the screenshot above, and volunteered that her son had been questioned by U.K. authorities in connection with the bitcoin theft.

Neither of the defendants families are disputing the basic claim that their kids stole from Mr. Schober. Rather, theyre asserting that time has run out on Schobers legal ability to claim a cause of action against them.

Plaintiff alleges two common law causes of action (conversion and trespass to chattel), for which a three-year statute of limitations applies, an attorney for the defendants argued in a filing on Aug. 6 (PDF). Plaintiff further alleges a federal statutory cause of action, for which a two-year statute of limitations applies. Because plaintiff did not file his lawsuit until May 21, 2021, three years and five months after his injury, his claims should be dismissed.

Schobers attorneys argue (PDF) that the statute of limitations begins to run when the Plaintiff knows or has reason to know of the existence and cause of the injury which is the base of his action, and that inherent in this concept is the discovery rule, namely: That the statute of limitations does not begin to run until the plaintiff knows or has reason to know of both the existence and cause of his injury.

The plaintiffs point out that Schobers investigators didnt pinpoint one of the young mens involvement until more than a year after theyd identified his co-conspirator, saying Schober notified the second boys parents in December 2019.

None of the parties to this lawsuit responded to requests for comment.

Image: Complaint, Schober v. Thompson, et. al.

Mark Rasch, a former prosecutor with the U.S. Justice Department, said the plaintiff is claiming the parents are liable because he gave them notice of a crime committed by their kids and they failed to respond.

A lot of these crimes are being committed by juveniles, and we dont have a good juvenile justice system thats well designed to both civilly and criminally go after kids, Rasch said.

Rasch said hes currently an attorney in a number of lawsuits involving young men whove been accused of stealing and laundering millions of dollars of cryptocurrency specifically crimes involving SIM swapping where the fraudsters trick or bribe an employee at a mobile phone store into transferring control of a targets phone number to a device they control.

In those cases, the plaintiffs have sought to extract compensation for their losses from the mobile phone companies but so far those lawsuits have largely failed to yield results and are often pushed into arbitration.

Rasch said it makes sense that some victims of cryptocurrency theft are spending some serious coin to track down their assailants and sue them civilly. But he said the legwork needed to make that case is tremendous and costly, and theres no guarantee those investments will pay off down the road.

These crimes can be monumentally difficult and expensive to track down, he said. Its designed to be difficult to do, but its also not designed to be impossible to do.

As evidenced by this weeks CNBC story on a marked rise in reports of people having their Coinbase accounts emptied by fraudsters, many people investing in cryptocurrencies find out the hard way that unlike traditional banking transactions cryptocurrency funds lost to theft are likely to stay lost because the transactions are irreversible.

Traditionally, the major crypto exchanges have said theyre not responsible for lost or stolen funds. But perhaps in response to the CNBC story, Coinbase said it was introducing a new pilot guarantee for U.K. customers only, wherein they will be eligible for a reimbursement of up 150,000 if someone gains unauthorized access to their account and steals funds.

However, it seems unlikely Coinbases new guarantee would cover cases like Schobers even if hed been a U.K. customer and the theft occurred today. One of the caveats that is not covered in the guarantee is sending funds to the wrong address by accident.

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Man Robbed of 16 Bitcoin Sues Young Thieves' Parents Krebs on Security - Krebs on Security

The team streamlines neural networks to become more familiar with computing encrypted data-ScienceDaily – Illinoisnewstoday.com

August 26, 2021 | Author: admin

This week, at the 38th International Conference on Machine Learning (ICML 21), researchers at NYU Cyber Security Center at NYU Tandon Institute of Technology unveil new insights into the basic functions that drive the ability of neural networks to make inferences. Encrypted data.

In the paper Deep ReDuce: ReLU Reduction for Fast Private Inference, the team focuses on linear and nonlinear operators, key features of neural network frameworks that place a heavy burden on time and computational resources depending on the operation. .. When neural networks compute encrypted data, many of these costs are incurred by the non-linear operation, the rectified linear activation function (ReLU).

A team of collaborators, including Brandon Regen, a professor of computer science and engineering, electrical and computer engineering, and a PhD in Nandan Kumar Jha. Students and Zahra Ghodsi, a former PhD student under the guidance of Siddharth Garg, have developed a framework called Deep ReDuce. We provide a solution by rearranging and reducing ReLU of neural networks.

Reagen explained that this shift requires a radical reassessment of where and how many components are distributed in the neural network system.

What were trying to do is rethink how neural networks are designed in the first place, he explained. By skipping many of these time-consuming and computationally expensive ReLU operations, you can get a high-performance network with 2-4 times faster execution times.

The team found that DeepReDuce improves accuracy and reduces ReLU counts by up to 3.5% and 3.5 times, respectively, compared to the latest private inference.

Surveys are not just academic. As the use of AI grows in tandem with the security concerns of personal, corporate, and government data security, neural networks are increasingly computing encrypted data. In such a scenario, which involves a neural network that generates private inference (PI) for hidden data without disclosing the input, it is the nonlinear function that offers the highest cost in time and power. These costs increase the difficulty and time it takes for the learning machine to perform PI, and researchers have struggled to reduce the burden that ReLU puts on such calculations.

The teams work is based on an innovative technology called CryptoNAS. The author is explained in previous treatises, including Ghodsi and a third PhD. Student Akshaj Veldanda, CryptoNAS, optimizes the use of ReLU because it may rearrange the way rocks are placed in streams to optimize water flow. Readjust the distribution of ReLUS in your network and remove redundant ReLUs.

DeepReDuce extends CryptoNAS by further streamlining the process. It consists of a series of optimizations for the wise removal of ReLU after the CryptoNAS reorganization feature. Researchers have tested by removing ReLU from traditional networks using DeepReDuce and found that it can significantly reduce inference latency while maintaining high accuracy.

Reagan collaborates with Mihalis Maniatakos, Research Assistant Professor of Electrical and Computer Engineering, with Duality, a data security company to design new microchips designed to handle calculations of fully encrypted data. It is also a part.

Research on ReLUS was supported by the ADA and the DARPA and Data Protection (DPRIVE) programs in virtual environments at the Center for Application-Driven Architecture.

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material Provided by NYU Tandon Institute of Technology.. Note: The content can be edited in style and length.

The team streamlines neural networks to become more familiar with computing encrypted data-ScienceDaily

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The team streamlines neural networks to become more familiar with computing encrypted data-ScienceDaily - Illinoisnewstoday.com

Last-mile routing research challenge awards $175000 to three winning teams – MIT News

August 26, 2021 | Author: admin

Routing is one of the most studied problems in operations research; even small improvements in routing efficiency can save companies money and result in energy savings and reduced environmental impacts. Now, three teams of researchers from universities around the world have received prize money totaling $175,000 for their innovative route optimization models.

The three teams were the winners of the Amazon Last-Mile Routing Research Challenge, through which the MIT Center for Transportation & Logistics (MIT CTL) and Amazon engaged with a global community of researchers across a range of disciplines, from computer science to business operations to supply chain management, challenging them to build data-driven route optimization models leveraging massive historical route execution data.

First announced in February, the research challenge attracted more than 2,000 participants from around the world. Two hundred twenty-nine researcher teams formed during the spring to independently develop solutions that incorporated driver know-how into route optimization models with the intent that they would outperform traditional optimization approaches. Out of the 48 teams whose models qualified for the final round of the challenge, three teams work stood out above the rest. Amazon provided real operational training data for the models and evaluated submissions, with technical support from MIT CTL scientists.

In real life, drivers frequently deviate from planned and mathematically optimized route sequences. Drivers carry information about which roads are hard to navigate when traffic is bad, when and where they can easily find parking, which stops can be conveniently served together, and many other factors that existing optimization models simply dont capture.

Each model addressed the challenge data in a unique way. The methodological approaches chosen by the participants frequently combined traditional exact and heuristic optimization approaches with nontraditional machine learning methods. On the machine learning side, the most commonly adopted methods were different variants of artificial neural networks, as well as inverse reinforcement learning approaches.

There were 45 submissions that reached the finalist phase, with team members hailing from 29 countries. Entrants spanned all levels of higher education from final-year undergraduate students to retired faculty. Entries were assessed in a double-blind review process so that the judges would not know what team was attached to each entry.

The third-place prize of $25,000 was awarded to Okan Arslan and Rasit Abay. Okan is a professor at HEC Montral, and Rasit is a doctoral student at the University of New South Wales in Australia. The runner-up prize at $50,000 was awarded to MITs own Xiaotong Guo, Qingyi Wang, and Baichuan Mo, all doctoral students. The top prize of $100,000 was awarded to Professor William Cook of the University of Waterloo in Canada, Professor Stephan Held of the University of Bonn in Germany, and Professor Emeritus Keld Helsgaun of Roskilde University in Denmark. Congratulations to all winners and contestants were held via webinar on July 30.

Top-performing teams may be interviewed by Amazon for research roles in the companys Last Mile organization. MIT CTL will publish and promote short technical papers written by all finalists and might invite top-performing teams to present at MIT. Further, a team led by Matthias Winkenbach, director of the MIT Megacity Logistics Lab, will guest-edit a special issue of Transportation Science, one of the most renowned academic journals in this field, featuring academic papers on topics related to the problem tackled by the research challenge.

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Last-mile routing research challenge awards $175000 to three winning teams - MIT News

Unless Congress acts, fewer Indian students will come to… – The American Bazaar

August 26, 2021 | Author: admin

Total green card backlog for Indians would increase to nearly 2.2 million by FY 2030, according to new NFAP study

Fewer Indian graduate students likely will come to America unless Congress addresses the long waits for employment-based green cards, fueled by the per-country limit and low annual quota, according to a new study.

Added to this is the rejection of 70% of H-1B registrations due to the inadequate 85,000-annual H-1B limit for companies, according to Forbes citing analysis of education data from the National Foundation for American Policy (NFAP).

US technology companies hire the greatest number of skilled workers from India and China every year through the H-1B visa for high skilled foreign nationals in specialty occupations.

For America to attract and retain top foreign-born talent, Congress and the executive branch almost certainly will need to change US policies, the NFAP study says.

Read: Include immigrants in green card backlog in budget reconciliation (August 24, 2021)

Even before the pandemic, US universities experienced declines in enrolling new international students. Covid-19 caused new enrolment to plummet, it noted.

The number of full-time international students enrolled in graduate-level electrical engineering at US universities declined 19.5% between 2015 and 2019.

The number of full-time international students enrolled in graduate-level computer and information sciences at US universities fell 9.5% between 2016 and 2019.

Between 50% and 82% of the full-time graduate students in key technical fields at US universities are international students.

Ominously, most of the graduate students are from India and Chinatwo countries where US policies are preventing or discouraging individuals from studying in America, the study suggests.

International students from India in graduate-level computer science and engineering at US universities dropped by more than 25% between the 2016-17 and 2018-19 academic years.

This decline is substantial for US universities, as 75% of full-time graduate students are international students, two-thirds of whom are from India, the National Law Review noted citing the NFAP study.

While the number of international students from India declined in the United States, the number in Canada rose from 76,075 in 2016 to 1,72,625 in 2018.

This is a 127% increase, as declared by the Canada Bureau for International Education, the Review noted.

As employment-based green cards are limited to 7% of the total issued in each fiscal year for each country, Indians wait for years and sometimes decades for their green card approvals.

Frustrated Indians have chosen to move to Canada from the United States, looking forward to a more permanent future, the review says.

Without the Congressional action, the total backlog for all three employment-based permanent resident categories for Indians would increase from an estimated 915,497 individuals currently to an estimated 2,195,795 by the fiscal year 2030, according to Stuart Anderson, NFAP Executive Director.

We should let that number sink in: Within a decade, more than two million people will be waiting in line for years or even decades for employment-based green cards, he testified before the House Judiciary Committee-Subcommittee on Immigration and Citizenship.

Highly skilled foreign nationals, including international students, are taking the informed decision of choosing Canada over America, the Review said.

The ease of acquiring temporary work visas for foreign nationals and subsequently acquiring permanent residence in Canada has pushed many foreign nationals to choose Canada.

The world has changed since 1990, the US immigration policy has not, Anderson was quoted as saying.

International students are a significant source of talent for US employers and allow US universities to offer high-quality academic programs in science and engineering for American students, according to the NFAP study.

Read: US lawmakers seek to include employment based Green Card backlog as part of budget reconciliation (August 24, 2021)

Without international students the number of students in America pursuing graduate degrees (Masters and PhDs) in fields such as computer and information sciences and electrical engineering would be small relative to the size of the US economy.

In 2019, at US universities, there were only 9,083 full-time American graduate students in electrical engineering, compared to 26,343 full-time international students.

Similarly, in computer and information sciences, in 2019, there were only 17,334 full-time American graduate students compared to 44,786 international graduate students at US universities, according to NFAP study.

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Unless Congress acts, fewer Indian students will come to... - The American Bazaar

Artificial Intelligence Is Changing The Face Of Tech In …

August 26, 2021 | Author: admin

The Tokyo Olympics recently concluded after being delayed for over a year due to coronavirus concerns. However, with the new schedule came the increased use of artificial intelligence to the table. The use of smart tech has completely altered the way AI was used in sports and fitness. Since the sports industry requires number crunching, it makes for an ideal setting for artificial intelligence. Let's see how AI is changing the sports industry for good.

While many things in the world are unpredictable, for things that can be predicted using data, there's AI. And, the world of sports is one such quantifiable thing. The use of AI in sports has become common in recent years. And, thanks to the positive impact it has left on the fitness industry, it will only continue to stretch its arms in the realm of sports.

As per PwC research, AI-based tools are already being used in almost all major sports disciplines such as cricket, baseball, soccer, and American football alongside non-professional leisure activities such as grassroots sports. These tools include sensors, wearables, and computer vision-powered cameras that gather data on athletes' performance.

Besides, natural language processing devices can make use of speech and text recognition to gain insights into the audience's sentiments. All this data can be crunched leveraging machine learning (ML) and deep learning (DL) systems to create forecast models and enable coaches to make better decisions. Let's get into the details.

Evaluating an athlete using quantitative metrics might not tell you a lot about them, but their performances can be subject to such scrutiny. Sports organizations are using this data as a measure of fitness and the potential of the athlete. However, the data used for recruiting doesn't mean using widely known stats of the person but using more complex metrics that consider other aspects as well.

The process of gathering data has become even more easy and reliable ever since big data and AI have taken over sports management. AI can use historical data to predict the future performance of players before recruiting them. The same process goes into predicting the market values of players before making a contract offer to new talent.

The analytical and predictive capabilities of AI go beyond just recruiting new players. They also find application in medical diagnostics, which is very imperative for players' performance. AI-backed tools can check for several physical parameters such as athletes' movements to determine the condition and spot injuries before the players even realize it.

Gathering information with sensors is necessary for data analytics in healthcare, and as per the latest trends, health wearables are the best products for that due to their portability and cheap price tags.

Their ability to track biometrics makes them more popular not just among professional athletes but also for fitness enthusiasts. As per MarketsandMarkets research, wearables are the biggest and fastest-growing share in the sports device market.

Besides making things easier for players and managers, AI is also capable of revolutionizing live broadcasting and impact the way viewers experience sports. Artificial Intelligence can also alter the way broadcasters make money from sporting events.

Moreover, AI systems can come in handy to automatically choose a suitable camera angle to provide the best viewing experience possible. It can smartly provide subtitles for live sporting events in viewers' preferred languages based on their whereabouts.

AI systems can also identify the best opportunities to push advertisements based on the reaction and excitement levels of the crowd. This will enable broadcasters to effectively make money through ad sales.

As they say "applause doesn't come without caveats," the use of AI systems could have some downsides. Data trading can be used for betting purposes as there is an immense amount of financial benefits involved. NCAA signed a 10-year contract with a UK IT company to collect and sell sports data to media corporations.

Another challenge could be keeping human talent intact since AI could take over sports such as car racing where results are most influenced by non-human elements. For instance, to win an F1 race, almost everything is data-driven, including the time taken during a pit stop.

Using data collected by onboard sensors is so important that Formula One teams use Amazon-powered cloud-computing services. This information can be stored and fed to AI to come up with the best strategies to succeed. Considering the pace at which AI is stretching its reach in sports, this trend can spread to all other sports, causing a clash between human talents and AI systems.

But we mustn't forget one thing that despite using AI to make predictions, we cannot rule out unpredictability and surprise from sports by virtue of the human element. After all, that's what makes sports exciting for viewers across the globe.

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Artificial Intelligence Is Changing The Face Of Tech In ...

Top Performing Artificial Intelligence (AI) Companies of 2021

August 26, 2021 | Author: admin

As artificial intelligence has become a growing force in business, todays top AI companies are leaders in this emerging technology.

Often leveraging cloud computing and edge computing, AI companies mix and match myriad technologies to meet and exceed use case expectations in the home, the workplace, and the greater community. Machine learning leads the pack in this realm, but todays leading AI firms are expanding their technological reach through other technology categories and operations, ranging from predictive analytics to business intelligence to data warehouse tools to deep learning, alleviating several industrial and personal pain points.

Entire industries are being reshaped by AI. RPA companies have completely shifted their platforms. AI in healthcare is changing patient care in numerous and major ways.

AI companies attract massive investment from venture capitalist firms and giant firms like Microsoft and Google that see the potential for further growth in corporate and personal use. Academic AI research is growing quickly in quantity and complexity, as are AI job openings across a multitude of industries. All of this growth and the exciting potential for new growth are documented in the AI Index, produced by Stanford Universitys Human-Centered AI Institute.

Consulting giant Accenture argues that AI has the potential to boost rates of profitability by an average of 38% and could lead to an economic boost of a whopping $14 trillion in additional gross value added (GVA) by 2035.

Especially during the COVID-19 pandemic, fields like healthcare have grown their interest and investment in AI, hoping to propel patient experiences forward in telemedicine, digital imaging, and a variety of other areas that give the patient greater access to medical resources they need.

Artificial intelligence clearly holds many possibilities, but IT professionals and other users should be cautious of a plethora of risks, such as job displacement. It will have a huge economic impact but also change society, and its hard to make strong predictions, but clearly job markets will be affected, said Yoshua Bengio, a professor at the University of Montreal, and head of the Montreal Institute for Learning Algorithms.

To keep up with the AI market, we have updated our list of top AI companies playing a key role in shaping the future of AI.

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Even during the COVID-19 pandemic where most industries reduced their total expenses to stay afloat, many companies actually increased their AI investments in 2020.

The AI vendors are leading the market by providing AI and ML through their popular cloud platforms, enabling companies to incorporate AI into applications and systems without the expense of in-house development.

The clear leader in cloud computing, AWS offers both consumer and business-oriented AI products and services, and many of its professional AI services build on the Ai services available in consumer products. Amazon Echo brings artificial intelligence into the home through the intelligent voice server, Alexa. For AWS, the companys primary AI services include Lex, a business version of Alexa; Polly, which turns text to speech; and Rekognition, an image recognition service.

Google, a leader in AI and data analytics, is on a massive AI acquisition binge, having acquired a number of AI startups in the last several years. Google is deeply invested in furthering artificial intelligence capabilities. In addition to using AI to improve its services, Google Cloud sells several AI and machine learning services to businesses. It has an industry-leading software project in TensorFlow, as well as its own Tensor AI chip project.

IBM has been a leader in the field of artificial intelligence since the 1950s. Its efforts in recent years center around IBM Watson, an AI-based cognitive service, AI software as a service, and scale-out systems designed for delivering cloud-based analytics and AI services. It has been acquisitive, purchasing several AI startups over several years. It benefits from having a strong cloud platform.

Microsoft offers a mix of consumer-facing and business/IT AI projects. On the consumer side, it has Cortana, the digital assistant that comes with Windows and is now available for smartphones other than Windows Phone, and the chatbot Zo that talks like a teenager. On its Azure cloud service, Microsoft sells AI services such as bot services, machine learning, and cognitive services.

The leading cloud computing platform in Asia, Alibaba offers clients a sophisticated Machine Learning Platform for AI. Significantly, the platform offers a visual interface for ease of use, so companies can drag and drop various components into a canvas to assemble their AI functionality. Also included in the platform are scores of algorithm components that can handle any number of chores, enabling customers to use pre-built solutions. Expect huge AI growth from Alibaba in the years to come.

These top AI providers are demonstrating that artificial intelligence can be used in a dazzling number of ways, across virtually every industry sector.

Palmer Luckey is one of the most intriguing figures in todays emerging tech. He co-founded Oculus, which Facebook bought for a cool $2 billion in 2014. Post-Facebook and at the ripe age of 27, he launched Anduril, which adds sophisticated sensors, vehicles, and drones to create a threat protection zone. Products include Sentry Tower (autonomous awareness), Ghost 4 sUAS (intelligent air support), and Anvil sUAS (precision kinetic intercept).

Formerly known as Sift Science, the company provides multiple online fraud management services in one platform. Sift mines thousands of data points from around the web to train in detecting fraud patterns. Its machine learning tools, bolstered by data analytics, seek insight into fraud before it happens.

Nauto offers an AI-powered driver behavior learning platform. So instead of self-driving cars, Nauto is an AI technology designed to improve the safety of commercial fleets and autonomous fleets. The platform assesses how drivers interact with the vehicle and the road ahead to reduce distracted driving and prevent collisions.

Tempus data-driven precision medicine uses AI to fight disease and bolster patient outcomes. It gathers and analyzes massive pools of medical and clinical data at scale to provide precision medicine that personalizes and optimizes treatments to each individuals specific health needs. Applications include neurology, psychiatry, and oncology.

In recent years, Salesforce has acquired a handful of AI companies and sharpened features of Salesforce Einstein, their artificial intelligence service. Their latest initiative, which includes an extensive team of data scientists, uses machine learning to help employees more efficiently perform tasks by simplifying and speeding them up. In addition to Salesforces employees, Einstein is available for customers who can build their own applications and are interested in features like Recommendation Builder, scorecards, and in-depth navigation insights.

A dominant vendor in the small but growing Robotic Process Automation market it actually coined the term RPA Automation Anywhere makes great use of AI. Its applications include attended RPA, which helps office employees do mundane, repetitive tasks much more efficiently, employing the power of machine learning. A vendor to watch.

SenSat builds digital copies of physical environments and applies AI modeling to understand the parameters of that environment and provide valuable feedback. For example, it can give spatial and volume statistics about a roadway that is about to undergo repair work. Boosting its fortunes, in October 2019, Tencent led a $10 million investment in SenSat.

Phrasee specializes in natural language generation for marketing copy. Its natural language generation system can generate millions of human-sounding variants of marketing at the touch of a button, allowing customers to tailor their copy to targeted customers. Retail/marketing and AI is a combination on a rapid growth curve in the AI sector. During the COVID-19 pandemic, several retailers, such as Walgreens, used Phrasee to boost customer engagement related to vaccination.

Using a combination of human freelancers and a system built with machine learning automation, Defined Crowd provides a data set that companies can leverage to improve the performance of their algorithms. This union of the human with AI is a brilliant stroke other startups are catching on, and you can expect many more startups to test out this combo.

Based in New York City, Pymetrics leverages AI to help companies hire the optimal candidates, by examining more than a resume scan. Customers have their best employees fill out the Pymetrics assessment, which then creates a model for what future ideal candidates should bring to the table. In essence, the AI-based system is attempting to find more new staff that will fit in well with the existing top staff, using AI and behavioral science.

Siemens, the famed legacy German multinational, focuses on areas like energy, electrification, digitalization, and automation. They also work to develop resource-saving and energy-efficient technologies and are considered a leading provider of devices and systems for medical diagnosis, power generation, and transmission. Yes, the Siemens website actually refers to AI at the beer garden.

Given how lucrative it is for hackers, will identity theft ever go away? Its unlikely, but New York City-based Socure is using AI to fight it. Its AI-enabled system monitors and checks the quality of countless data sources far more than a human, of course, but more importantly, far more than a legacy system that doesnt have the speed, flexibility, and insight of AI. Its motto is identify more real people in real-time. Socure was named a Cool Vendor 2020 in Gartners Cool Vendors in AI for Banking and Investments.

AEye builds the vision algorithms, software, and hardware used to guide autonomous vehicles. Its LiDAR technology focuses on the most important information in a vehicles sightline, such as people, other cars, and animals, while putting less emphasis on other landscape features like the sky, buildings, and surrounding vegetation. In February 2021, AEye entered into a merger agreement with CF Finance Acquisition Corp. III, so if/when the deal closes, expect more investment and innovation in the near future.

In a world with a vast ocean of podcasts and videos to transcribe, Rev uses AI to find its market. An AI-powered but human-assisted transcription provider, the company also sells access to developers, so tech-savvy folks can use its speech recognition technology. But the key part here is the combination of humans with AI, which is a sweet spot in the effective use cases for artificial intelligence. With a growing need for accessibility features in audiovisual production especially, expect more AI companies to take advantage of a similar business model in the future.

Its not enough that Suki offers an AI-powered software solution that assists doctors as they make voice notes on a busy day. Sukis aim using the power of AI to learn over time is to mold and adapt to users with repeated use, so the solution becomes more of a time saver and efficiency booster for physicians and healthcare workers over time. As a sign of the times, Suki was delivered with COVID-19 data and templates to speed the critically important vaccination and health tracking processes.

In the future, everything will be tracked by intelligent cameras. Verkada is working to create that future by offering a network of AI-assisted cameras that can handle sophisticated movement monitoring, through a software-first approach to security. Given all the uses for such cameras, which employ the cloud, its no surprise that the companys clients range from schools to shopping malls.

DataVisor uses machine learning to detect fraud and financial crime, utilizing unsupervised machine learning to identify attack campaigns before they result in any damage. DataVisor protects companies from attacks such as account takeovers, fake account creation, money laundering, fake social posts, fraudulent transactions, and more.

Founded in 2016, People.ais goal is to streamline the life of salespeople, assisting them in putting the reams of small details into relevant CRM systems, chiefly Salesforce. Think of all those pesky info bits from texting, your calendar, endless Slack conversations People.ai aims to help you with all of that. Plus: the system attempts to coach sales reps on the most effective ways to manage their time.

AlphaSense is an AI-powered search engine designed for investment firms, banks, and Fortune 500 companies. The search engine focuses on searching for important information within earnings call transcripts, SEC filings, news, and research. The technology also uses artificial intelligence to expand keyword searches for relevant content.

The remarkable truth about AI is that it keeps moving up the food chain in terms of the sophisticated tasks it can handle. Taking a big step up from simple automation, Icertis with a decade under its belt handles millions of business contracts through a method they call contract intelligence. Leveraging the cloud, the companys solution automates certain tasks and scans previous contract details. The company has gained some big clients like Microsoft and has been named a Gartner 2020 Leader.

Casetext is an AI-powered legal search engine that specializes in legal documents, with a database of more than 10 million statutes, cases, and regulations. A recent study comparing legal research platforms found that attorneys using Casetexts CARA AI finished their research more than 20% faster, required 4.4 times fewer searches to accomplish the same research task, and rated the cases they found as significantly more relevant than those found with a legacy research tool.

Blue River Technology is a subsidiary of Deere & Co. that combines artificial intelligence and computer vision to build smart farm tech clearly a growing need, given population growth. The companys See & Spray technology can detect individual plants and apply herbicide to the weeds only. This reduces the number of chemicals sprayed by up to 90% over traditional methods.

Nvidias emergence as an AI leader was hardly overnight. It has been promoting its CUDA GPU programming language for nearly two decades. AI developers have come to see the value in the GPUs massively parallel processing design and embraced Nvidia GPUs for machine learning and artificial intelligence. One area Nvidia is making a big push is in self-driving cars, but it is one of many efforts on the horizon.

Automation in factories has been progressing for years, even decades, but Bright Machines is working to push it a quantum leap forward. Based in San Francisco, the AI company is leveraging advances in robotics like machine learning and facial recognition to create an AI platform for digital manufacturing. Its solutions can accomplish any number of fine-grain tasks that might previously have required the exactitude of a skilled human.

Orbital Insight uses satellite geospatial imagery and artificial intelligence to gain insights not visible to the human eye. It uses data from satellites, drones, balloons, and other aircraft to look for answers or insight on things related to the agriculture and energy industries that normally wouldnt be visible. The company touts itself as the leader in geospatial analytics.

Once a standalone company and now a division of MasterCard, Brighterion offers AI for the financial services industry, specifically designed to block fraud rates. The companys AI Express is a fast-to-market solution within 6-8 weeks that is custom designed for customer use cases. Its solution is used by the majority of the 100 largest banks.

H2O.ai provides an open-source machine learning platform that makes it easy to build smart applications. Used by many thousands of data scientists across a large community of organizations worldwide, H2O claims to be the worlds leading open-source deep learning platform. H20.ai provides solutions for insurance, healthcare, telecom, marketing, financial service, retail, and manufacturing.

With a long legacy as the top chipmaker, Intel has both hardware and software AI initiatives in the works. Its Nervana processor is a deep learning processor, while Movidius is geared toward neural networks and visual recognition. Intel is also working on natural language processing and deep learning through software and hardware. Further indicating their commitment to AI, one of the companys slogans is accelerate your AI journey with Intel.

Clarifai is an image recognition platform that helps users organize, filter, and search their image database. Images and videos are tagged, teaching the technology to find similarities in images. Its AI solution is offered via mobile, on-premise, or API. Beyond image recognition, Clarifai also offers solutions in computer vision, natural language processing, and automated machine learning.

Geared to assist the busiest of people, X.ais intelligent virtual assistant Amy helps users schedule meetings. The concept is simple if you receive a meeting request but dont have time to work out logistics, you copy Amy onto the email and she handles it. Through machine learning and natural language processing, Amy schedules the best time and location for your meeting based on your preferences and schedule. We all need a helper like this in our lives.

Zebra Medical Systems is an Israeli company that applies deep learning techniques to the field of radiology. It claims it can predict multiple diseases with better-than-human accuracy by examining a huge library of medical images and specialized examination technology. It recently moved its algorithms to Google Cloud to help it scale and offer inexpensive medical scans.

Iris.AI helps researchers sort through cross-disciplinary research to find relevant information, and as it is used more often, the tool learns how to return better results. Since its launch, countless people have tried the service, some becoming regular users. Its Iris.AI release includes the Focus tool, an intelligent mechanism to refine and collate a reading list of research literature, cutting out a huge amount of manual effort.

Freenome uses artificial intelligence to conduct cancer screenings and diagnostic tests to spot signs of cancer earlier than possible with traditional testing methods. It uses non-invasive blood tests to recognize disease-associated patterns. The companys solution has trained on cancer-positive blood samples, which enable it to detect problems using specific biomarkers.

Neurala claims that it helps users improve visual inspection problems using AI. It develops The Neurala Brain, a deep learning neural network software that makes devices like cameras, phones, and drones smarter and easier to use. AI tends to be power-hungry, but the Neurala Brain uses audio and visual input in low-power settings to make simple devices more intelligent.

Graphcore makes what it calls the Intelligence Processing Unit (IPU), a processor specifically for machine learning, used to build high-performance machines. The IPUs unique architecture allows developers to run current machine learning models orders of magnitude faster and undertake entirely new types of work not possible with current technologies.

CognitiveScale builds customer service AI apps for the healthcare, insurance, financial services, and digital commerce industries. Its products are built on its Cortex-augmented intelligence platform for companies to design, develop, deliver, and manage enterprise-grade AI systems. It also has an AI marketplace, which is an online AI collaboration system where business experts, researchers, data scientists, and developers can collaborate to solve problems.

iCarbonX is a Chinese biotech startup that uses artificial intelligence to provide personalized health analyses and health index predictions. It has formed an alliance with seven technology companies from around the world that specialize in gathering different types of healthcare data and will use algorithms to analyze genomic, physiological, and behavioral data. It also works to provide customized health and medical advice.

Human Resources can be a bifurcated digital workspace, with different apps for each task that HR handles. OneModel is a talent analytics accelerator that helps HR departments handle employees, career pathing, recruiting, succession, exits, engagement, surveys, HR effectiveness, payrolls, planning, and other HR features all in one place and in a uniform way. The companys core goal is to equip HR pros with machine learning smarts.

AI meets social media. Lobster Media is an AI-powered platform that helps brands, advertisers, and media outlets find and license user-generated social media content. Its process includes scanning major social networks and several cloud storage providers for images and video, using AI-tagging and machine learning algorithms to identify the most relevant content. It then provides those images to clients for a fee.

Next IT, now part of Verint, is one of the pioneers in customer service chatbots. It develops conversational AI for customer engagement and workforce support on any endpoint through intelligent virtual assistants (IVAs). The companys Alme platform powers natural language business products that are continually enhanced through AI-powered tools that empower human trainers to assess performance and end-user satisfaction.

Pointr is an indoor positioning and navigation company with analytics and messaging features that help people navigate busy locations, like train stations and airport terminals. Its modules include indoor navigation, contextual notifications, location-based analytics, and location tracking. Its Bluetooth beacons use customer phones to help orient them around the building.

One of the largest social media companies to come out of China, Tencent has an advanced AI lab that developed tools to process information across its ecosystem, including natural language processing, news aggregators, and facial recognition. They also have one of Chinas top video streaming platforms, Tencent Music. A giant in the field, they fund several AI efforts.

A fairly new startup in the AI copywriting space, Copy.ai uses basic inputs from users to generate marketing copy in seconds. It can create copy for a variety of different formats, including article outlines, meta descriptions, digital ads and social media content, and sales copy. In March 2021, it was announced that Copy.ai raised $2.9 million in investments from Craft Ventures and several other smaller investors. With its use of the GPT-3 language model to generate words, Copy.ai is a content-driven AI tool to keep an eye on.

Twilio is a cloud communications platform as a service (PaaS) company that allows software developers to integrate text messages, phone calls, and video calls into applications through the use of various APIs. Twilios services are accessed over HTTP and are billed based on usage. The Twilio Autopilot offering allows companies to build and train AI-driven chatbots.

ViSenzes artificial intelligence visual recognition technology works by recommending visually similar items to users when shopping online. Its advanced visual search and image recognition solutions help businesses in eCommerce, mCommerce, and online advertising by recommending visually similar items to online shoppers.

Based in Asia, SenseTime develops facial recognition technology that can be applied to payment and picture analysis. It is used in banks and security systems. Its valuation is impressive, racking several billion dollars in recent years. The company specializes in deep learning, education, and fintech.

Using machine learning to mine health data for cancer research, Flatiron finds cancer research information in near real-time, drawing on a variety of sources. The company raised more than $175 million in Series C funding before being acquired by cancer research giant Roache.

Deep 6 uses AI to, in its own words, find more patients in minutes, not months. The patients in this sense are participants in clinical trials a critical part of the research process in developing new medicine. Certainly one of the challenging issues that was faced during the quest for a COVID-19 vaccine was finding a community of appropriate candidates. Deep 6 finds these kinds of communities by using an AI-powered system to scan through medical records, with the ability to understand patterns in human health.

Considered one of the best AI-driven customer support tools out there, Directly counts Microsoft as a customer. It helps its customers by intelligently routing their questions to chatbots to answer their questions personally, or to customer support personnel. It prides itself on intelligent automation.

Based in Montreal, Element AI provides a platform for companies to build AI-powered solutions, particularly for firms that may not have the in-house talent to do it. Element AI says it supports app-building for predictive modeling, forecasting modeling, conversational AI and natural language processing, image recognition, and automatic tagging of attributes based on images. The company was founded in 2016.

Pony.ai develops software for self-driving cars and was created by ex-Google and Baidu engineers who felt that the big companies are moving too slow. It has already made its first fully autonomous driving demonstration. It now operates a self-driving ride-sharing fleet in Guangzhou, China, using cars from a local automaker. The company raised $400 million from Toyota.

Focusing on enterprise AI, C3.ai offers a wide array of pre-built applications, along with a PaaS solution, to enable the development of enterprise-level AI, IoT applications, and analytics software. These AI-fueled applications serve a wide array of sectors and industry verticals, from supply chains to healthcare to anti-fraud efforts. The goal is to speed and optimize the process of digital transformation.

Some of the best applications of AI look into the future to prevent future problems. Such is the goal with BigPanda, which leverages AI to lessen or stop IT outages before they take down a full business, an eCommerce operation, or a mission-critical application. In essence, this companys goal is the magic of AIOps, using AI to improve admin and IT operations. A major growth area.

Accubits, a top-rated AI development company, focuses most of its energy on helping businesses enable AI for new efficiencies in their existing systems. Some of their AI solutions include intelligent chatbots in CRMs and predictive health diagnostics, both of which are designed to mesh with your existing software infrastructure. Accubits works across industries like consumer technology, automotives, cybersecurity, healthcare, and fashion.

Stem is a veteran energy storage firm that has adopted AI to help automate energy management. It uses its industry-leading AI platform, Athena, to determine when to charge energy storage systems and when to draw on them. Athena focuses on energy forecasting and automated control.

The robots imagined by 1950s futurists were tin men that could walk and talk and probably become masters of the human race. It hasnt turned out that way (fortunately), but Bossa Nova Robotics is using AI to make todays robots more effective. Indeed, modern robots are rarely shaped like humans; Bossa Novas robots resemble tall vacuum cleaners. Ironically, Bossa Nova started as a robotic toymaker but now has full-scale robots in retailers like Walmart. The robots roll up and down the shelves, spotting inventory problems and allowing cost savings on human workers.

In a world run by data, in many cases, someone or some system has to prep that data so that its usable. Data prep is unglamorous but absolutely essential. Tamr combines machine learning and human tech staff to help customers optimize and integrate the highest value datasets into its operations. Referred to as an enterprise-scale data unification company, Tamr enables cloud-native, on-premise, or hybrid scenarios truly a good fit for todays data-driven, multi-cloud world.

Formerly known as InsideSales.com, Xant underwent a major rebrand and now focuses on the enterprise market. It is a sales acceleration platform with a predictive and prescriptive self-learning engine, assisting in a sale and providing guidance to the salesperson to help close the deal. At its core is machine learning.

Dataminr is a global real-time information discovery company that monitors news feeds for high-impact events and critical breaking news far faster than your Google newsfeed. It cuts through the clutter of non-news or irrelevant news to specific industries and only provides highly relevant news when it happens. For news-sensitive vendors, its goal is to detect early risks from media coverage.

Theres a gray area in our lives in terms of healthcare; we ask ourselves, does this problem Im having really require making a doctors appointment, or could a major dose of simple information be enough? K Healths AI solution operates in this area. Users can text with a doctor or find similar cases near them, which has been particularly useful for COVID-19. Using a model built from a vast store of anonymous health records, its system offers help based on how a users complaint correlates with this vast history of other patients. Think of K Health as the advanced edge of telemedicine.

Driving the AI revolution with the highly capable smartphone chips it makes, Qualcomm leverages a signal processor for image and sound capabilities. In March 2021, Qualcomm acquired NUVIA, a competitive CPU and technology design company, ultimately enhancing CPU opportunities for the future. Given its market size and power, its likely that Qualcomm will continue to be a key driver of AI functionality in the all-important consumer device market.

HyperScience is designed to cut down on the tedium of mundane tasks, like filling out forms or data entry of hand-written forms. It also processes the relevant information from forms rather than requiring that a human read through the whole form. It touts itself as intelligent document processing.

Vivints Smart Home is a popular smart home service in North America, with features like security cameras, heating and cooling management, door and window security, and a remote speaker to talk to people at the door. All of this is monitored by AI, which learns the residents behavioral patterns and adjusts management accordingly.

While Facebook is certainly better known in other areas as one of the largest social media networks in the world, the company is making great strides in its AI capabilities, especially in self-teaching for its newsfeed algorithms. Most significantly, the Facebook team has started using AI to screen for hate speech, fake news, and potentially illegal actions across posts on the site.

Symphony Ayasdi is a machine intelligence software company that offers intelligent applications to its clients around the world for using Big Data and complex data analytics problems. Its goal is to help customers automate what would be the manual processes of using their own unique data. In March 2021, Symphony AyasdiAI announced a new partnership with Sionic, leading to a greater focus on financial crime detection. Very much focused on the enterprise AI sector.

A well-known technology company in the contract world, DocuSign uses esignature technology to digitize the contracting process across a multitude of industries. Many users dont realize some of the AI features that DocuSign powers, such as AI-powered contract and risk analysis that gets applied to a contract before you sign. This AI process lends itself to more efficient contract negotiation and/or renegotiations.

This cloud-based SaaS firm focuses on endpoint security. Leveraging AI, CrowdStrikes Falcon platform enables it to identify what it calls active indicators of attack to detect malicious activity before a breach actually happens. It presents the network administrators with actionable intelligence of real-time findings for them to take necessary action.

Cylance, now a division of BlackBerry, develops security apps that prevent instead of reactively detecting viruses and other malware. Using a mathematical learning process, Cylance identifies what is safe and what is a threat rather than operating from a blacklist or whitelist. The company claims its machine learning has an understanding of a hackers mentality to predict their behavior.

Tetra Tech uses AI to take notes on phone calls, so people working in call centers can focus on discussions with the callers. It uses AI to generate a detailed script of dialogues using its speech recognition technology. Given the large market for call centers and the need to make them more effective at low cost this is a big market for AI.

Nuro makes very small self-driving electric delivery trucks designed for local deliveries, such as groceries or takeout. Its founders previously worked on Googles Waymo self-driving car project. Overall the companys goal is to boost the value of robotics in daily life.

SoundHound started as a Shazam-like song recognition app called Midomi, but it has expanded to answering complex voice prompts like Siri and Cortana. But instead of converting language into text like most virtual assistants, the apps AI combines voice recognition and language understanding into a single step.

Acquired in a $1.2 billion high profile deal by Amazon, Zoox is focused on self-driving cars or, in the larger sense, a self-driving fleet (hence Amazons interest). Their AI-based vehicle is geared for the robo-taxi market.

Founded in 2013, AI biotech company Zymergen describes itself as a biofacturer. One of their offerings is called Hyline, a bio-based polyimide film. Their work includes applications for pharmaceuticals, agriculture, and industrial uses. Based in Emeryville, California.

A company designed to help digital advertisers run targeted digital advertising campaigns, The Trade Desk uses AI to optimize its customers advertising campaigns for their appropriate audiences. Their AI, known as Koa, was built to analyze data across the internet to figure out what certain audiences are looking for and where ads should be placed to optimize reach and cost. The Trade Desk also allows you to launch your digital ads independently, but uses its AI to offer performance suggestions while your campaign is live.

Based in China, DJI is a big player in the rapidly growing drone market. The company is leveraging AI and image recognition to track and monitor the landscape, and its expected that the company will play a role in the self-driving car market. Impressively, DJI has partnered with Microsoft for a drone initiative.

Running AI is exceptionally data-intensive the more data the better and so todays chipmakers (like Intel and Nvidia) are star players. Add to that list HiSilicon. The company fabricated the first AI chip for mobile units. Impressively, the chip accomplishes tasks like high-speed language translation and facial recognition.

Insitro operates at the convergence of human biology and machine learning. More specifically, it uses artificial intelligence to build models of various human illnesses, using those models to forecast previously unknown solutions far beyond human intuition. These models use the power of ML to improve drug discovery and development. Founded by Daphne Koller, Insitro has drawn investment from an exhaustive array of VC and financial firms.

A leading RPA company, Blue Prism uses AI-fueled automation to do an array of repetitive, manual software tasks, which frees human staff up to focus on more meaningful work. The companys AI laboratory researches automated document reading and software vision. To further boost its AI functionality, Blue Prism bought Thoughtonomy, which has AI based in the cloud.

You have surely encountered the limited conversational elan of a chatbot; a few stock phrases delivered in a monotone. Rulai is working to change this using the flexibility and adaptability of AI. The company claims its level 3 AI dialog manager can create multi-round conversation, without requiring code from customers. Clearly a major growth area.

Think of these forward-looking AI companies as taking a particularly inventive approach to machine learning and AI.

OpenAI is a non-profit research firm that operates under an open-source type of model to allow other institutions and researchers to freely collaborate, making its patents and research open to the public. The founders say they are motivated in part by concerns about existential risk from artificial general intelligence.

With backing by some real heavyweights Jeff Bezos, Elon Musk, and Mark Zuckerberg Vicariouss goal is nothing less than to develop a robot brain that can think like a human. It hasnt been particularly forthcoming with details, but its AI robots, geared for industrial automation, are known to learn as they do more tasks.

Arguably the coolest application of AI on this entire list, Ubiquity6 has built a mobile app that enables augmented reality for several people at once. Users see and interact with objects presented by the fully dimensioned visual world of the Ubiquity app, immersing themselves in a creative or educational environment. The companys website is worth visiting for its visual creativity and wonderment alone.

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Top Performing Artificial Intelligence (AI) Companies of 2021

Elon Musk unveils plans for humanoid robot that uses Tesla …

August 26, 2021 | Author: admin

Tesla Inc. showcased its artificial-intelligence systems on Thursday amid renewed criticism for Autopilot, its most-talked-about AI-based system, as it unveiled its next big project: a humanoid robot.

At the companys first AI Day, Chief Executive Elon Musk gave a preview of the Tesla Bot, a 5-foot-8-inch robot with a screen for a face, weighing about 125 pounds and capable of moving about 5 mph slow enough for people to run away from and small enough so a human could overpower it, Musk joked. He said a prototype is expected next year.

Musk said building a humanoid robot is a logical next step for Tesla, since, he said, its already the worlds biggest robotics company, with its cars basically robots. The humanoid robot will use all the tools in Teslas vehicles sensors, cameras, neural networks, etc. to autonomously navigate the outside world.

Were making the pieces that would be useful for a humanoid robot, so we should probably make it. If we dont, someone else will and we want to make sure its safe, Musk said.

I think this will be quite profound, Musk added, speculating that the robot could eventually change how the world works. While it could be used for things as basic as household chores, its intended for unsafe, repetitive or boring tasks, he said. Basically, what is the work people would least like to do?

In the future, physical labor will be a choice, Musk said, adding that that will likely result in a universal basic income, someday.

Musk said he hopes the robot is not perceived as something dystopian, and that it could even be your friend. When asked in a Q&A session about the possibility of superhuman AI eventually running amok, Musk said that while thats a concern, Tesla is aiming to make useful, or narrow, AI that will be used unequivocally for good.

When asked how Tesla expects to find a consumer market for the robot, Musk quipped Well, youll just have to see.

Earlier in the presentation, Andrej Karpathy, director of Artificial Intelligence and Autopilot Vision at Tesla, delivered a highly technical explanation of Teslas neural network, aka the brain of Teslas vehicles, and laid out in detail how Tesla uses cameras and AI for predictive learning. Other Tesla AI executives laid out Teslas technological breakthroughs in labeling data and building a super-fast computer to train Autopilot.

In addition to showing off its technology, AI Day served as a recruitment event, and Musk encouraged prospective hires to apply for hardware or software jobs at Tesla. Join our team, help build this, he said.

The event came as federal regulators have launched another investigation into Autopilot, Teslas advanced driver-assistance system, following several crashes involving parked emergency-response vehicles and emergency scenes.

Tesla shares TSLA, +0.38% ended the regular trading day down 2.3%, and edged up 0.4% in Fridays premarket session.

The National Highway Traffic Safety Administration also has opened several investigations related to Autopilot, including some that resulted in deaths. Alleged Autopilot malfunctions have sparked more than a dozen lawsuits in the U.S.

On Monday, two U.S. senators called on the Federal Trade Commission to look into whether Tesla misleads consumers by overstating Autopilots capabilities.

Tesla has long said Autopilot makes driving safer, and that it makes it clear that drivers have to be alert and prepared to take over at any time upon engaging Autopilot. Musk said during Thursdays presentation that Autopilots basic purpose is to avoid crashes, and said it does that very well.

Dont miss: Opinion: Its time for Elon Musk to start telling the truth about autonomous driving

For equally long, however, critics have said the system gives some drivers a false sense of security and implies self-driving abilities well beyond its real-world capabilities.

Musk is known for his bold claims that dont always pan out, such as promising for years a completely autonomous Los Angeles-to-New York trip, and plans for a fleet of robotaxis as early as this year.

Musk first announced the idea of holding an AI Day on Twitter in June, saying it would help with Teslas recruiting efforts.

Tesla has held similar special events in the past, including ones to unveil new vehicles or new vehicle trims, like the one held in June to reveal the Model S Plaid, and another to highlight its battery technology in September 2020.

Tesla stock has lost about 4% this year, contrasting with gains of more than 17% for the S&P 500 index SPX, +0.22%.

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Elon Musk unveils plans for humanoid robot that uses Tesla ...

10 Famous People in Artificial Intelligence

August 26, 2021 | Author: admin

Artificial intelligence is rapidly excelling at many human tasks, such as medical diagnosis, language translation, and customer support. This is creating legitimate concerns that AI will eventually displace human employees throughout the economy. But it is hardly the only, or even the most likely, consequence. Never once have digital technologies been so attentive to us, nor have we been so reactive to our devices. As AI will fundamentally transform how and who performs work, the technologys greater effect will be in complementing and boosting human talents rather than replacing them.

The AI revolution has already started, and it is changing every area of our life.

From academics and researchers to inventive tech entrepreneurs, here are 10 people pushing the frontiers of deep learning and computerized vision to make AI dreams come true.

Musks emphasis at Open AI, his non-profit organisation, is on creating safe artificial general intelligence (AGI) in a manner that enhances humanity, despite the possible risks of AI. They do groundbreaking research and develop open-source tools for experimentation, such as OpenAI Universe.

Musk wants to create a neural implant that connects the human brain to artificial intelligence, allowing people to operate computers, artificial limbs, and other equipment with their thoughts alone.

Schjll Bredes objective with her AI scientist, Iris.ai, is to speed up research. The technology searches through over 60 million papers for the most relevant articles using algorithms. Her goal isnt to make money, but to do good and improve peoples lives, such as by discovering a cancer treatment.

Schmidhuber is known as the Father of Self-Aware Robots for developing the mechanisms that allow us to communicate to our phones. His current research focuses on developing artificial neural networks that are equivalent to, and eventually surpass, the human brain.

Li, a well-known computer vision professor, is on a mission to democratise AI to guarantee that talent and expertise are shared outside large corporations in order to increase diversity, creativity, and innovation. AI4ALL, her non-profit, trains the next generation of AI technologists, philosophers, and entrepreneurs.

Selzs firm, Squirro, is the pioneer in real contextual intelligence, promoting data as the worlds most expensive resource. Artificial intelligence and machine learning are used to offer the why behind data, resulting in actionable intelligence and a deep knowledge of customers.

Socher has been dubbed one of the artificial intelligence and machine learning spaces virtuosos. By providing a deeper grasp of context and mood, his methods are revolutionising natural language processing and computer vision.

Hassabis, dubbed the superhero of artificial intelligence, is advancing AI by reuniting it with cognitive science. He works at DeepMind on innovative algorithms that can help mankind in areas like healthcare and environmental issues.

Cucchiara has almost 300 publications to his credit. Her work in computer vision, multimedia recognition systems, machine learning, smart sensing, and human behaviour understanding (HBU) has had a significant impact in assisting businesses in pushing AI limits.

Sensorbeat, the first comprehensive package comprising all the electronic devices and AI for interpreting the motion of items and people, was conceived and developed by Hardebrings team. Real-time applications are enabled by smart data processing embedded into the gadget itself.

Li wants to be in charge of AIs destiny in China and abroad. Baidu is constantly engaged in cutting-edge research in fields such as artificial intelligence, computer vision, and machine learning. Baidu Brain is a set of AI-powered solutions, as well as DuerOS, a smart speaker platform.

One of the most often mentioned advantages of AI technology is automation, which has had substantial effects on the communication, transport, consumer goods, and service sectors. Automation not only provides for higher production levels and efficiency in various industries, but it also allows for more efficient input materials usage, better product quality, shorter lead times, and improved safety. Automation can also assist to free up resources that can be put to better use.

Artificial Intelligence has long been used to help businesses make better decisions. To make good judgments for the organisation, AI technology can coordinate data supply, evaluate trends, establish data consistency, give predictions, and quantify uncertainties. AI will stay neutral on the subject at hand as long as it is not trained to replicate human emotions, and it will assist in making the best choice to promote corporate efficiency.

AI-powered solutions can assist organisations in swiftly responding to consumer questions and concerns and resolving issues. Chatbots that combine conversational AI and Natural Language Processing technology may provide highly customised messages to consumers, assisting in the discovery of the optimal solution for their requirements. AI technologies can also assist customer care representatives feel less stressed, resulting in increased productivity.

Artificial Intelligence (AI) technologies are growing progressively popular in the healthcare industry. For example, remote patient monitoring technology enables healthcare practitioners to swiftly make clinical diagnosis and prescribe treatments without having the patient attend the hospital in person. AI can also help track the course of infectious diseases and even forecast their impacts and consequences in the future.

Artificial intelligence (AI) and machine learning (ML) may be used to evaluate data considerably more quickly. It can aid in the development of prediction models and algorithms for data processing and understanding the possible outcomes of various trends and events. Furthermore, AIs powerful computational skills may expedite the processing and interpretation of data for research and innovation that would otherwise take much longer for humans to examine and comprehend.

While many businesses have utilised AI to automate operations, those who use it primarily to replace workers will only experience short-term productivity improvements. In our 1,500-company study, we discovered that when people and robots collaborate, organisations make the most substantial performance gains. Humans and AI actively improve each others complimentary qualities through cooperative intellectual ability.

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10 Famous People in Artificial Intelligence

Artificial Intelligence vs. Machine Learning: Whats the …

August 26, 2021 | Author: admin

Artificial intelligence (AI) and machine learning (ML) are terms that have created a lot of buzz in the technology world, and for good reason. Theyre helping organizations streamline processes and uncover data to make better business decisions. Theyre advancing nearly every industry by helping them work smarter, and theyre becoming essential technologies for businesses to maintain a competitive edge.

These technologies are responsible for capabilities like facial recognition features on smartphones, personalized online shopping experiences, virtual assistants in homes, and even the medical diagnosis of diseases.

Demand for these technologiesand professionals skilled in themis booming. According to a report from research firm Gartner, the average number of AI projects in place at an organization is expected to more than triple over the next two years.

This exponential growth is posing problems for organizations. They report that their top challenges with these technologies include a lack of skills, difficulty understanding AI use cases, and concerns with data scope or quality.

AI and ML, which were once the topics of science fiction decades ago, are becoming commonplace in businesses today. And while these technologies are closely related, the differences between them are important. Heres a closer look into AI and ML, top careers and skills, and how you can break into this booming industry.

Whether you have a technical or non-technical background, heres what you need to know.

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Artificial intelligence is a poorly defined term, which contributes to the confusion between it and machine learning, says Bethany Edmunds, associate dean and lead faculty for Northeasterns computer science masters program.

Artificial intelligenceis essentially a system that seems smart. Thats not a very good definition, though, because its like saying that something is healthy. What exactly does that mean? she says. On a basic level, artificial intelligence is where a machine seems human-like and can imitate human behavior.

These behaviors include problem-solving, learning, and planning, for example, which are achieved through analyzing data and identifying patterns within it in order to replicate those behaviors.

Machine learning, on the other hand, is a type of artificial intelligence, Edmunds says. Where artificial intelligence is the overall appearance of being smart, machine learning is where machines are taking in data and learning things about the world that would be difficult for humans to do, she says. ML can go beyond human intelligence.

ML is primarily used to process large quantities of data very quickly using algorithms that change over time and get better at what theyre intended to do. A manufacturing plant might collect data from machines and sensors on its network in quantities far beyond what any human is capable of processing. ML is then used to spot patterns and identify anomalies, which may indicate a problem that humans can then address.

Machine learning is a technique that allows machines to get information that humans cant, she says. We dont really know how our vision or language systems workits difficult to articulate in an easy way. For this reason, were relying on data and feeding it to computers so they can simulate what they think were doing. Thats what machine learning does.

Because artificial intelligence is a catchall term for smart technologies, the necessary skill set is more theoretical than technical. Machine learning professionals, on the other hand, must have a high level of technical expertise.

People pursuing a career in artificial intelligence must have a foundation in:

People pursuing a career in machine learning must have a foundation in:

According to the World Economic Forums The Future of Jobs 2018report, there will be 58 million new jobs in artificial intelligence by 2022and a shortage of skilled professionals to fill them, according to Gartner. The following are the most in-demand jobs that require artificial intelligence and machine learning skills, according to a report from jobs site Indeed.

Machine learning engineers are advanced programmers tasked with developing AI systems that can learn from data sets. These professionals need to have strong data management skills and the ability to perform complex modeling on dynamic data sets.

These professionals are computer scientists who use deep learning platforms to develop programming systems that mimic brain functions. Experience developing neural networks is a must.

A senior data scientist uses the businesss data to enhance business capabilities using advanced statistical procedures. These are highly skilled computer scientists and specialized mathematicians who are responsible for the collection and cleaning of data. They may use experimental frameworks for product development and machine learning to lay a strong foundation for advanced analytics. They are also responsible for monitoring junior data scientists and for driving the organization toward a data-driven culture.

A computer vision engineer determines how a computer can be programmed to achieve a higher level of understanding through the processing of digital images or videos. Computer vision uses massive data sets to train computer systems to interpret visual images.

Learn More: 5 High-Paying Careers in Artificial Intelligence

Northeastern University offers two avenues for people looking to pursue an advanced degree in artificial intelligence: a Master of Science in Artificial Intelligence (MSAI) and a Master of Science in Computer Science (MSCS) with a specialization in artificial intelligence.

The MSAI does not require a computer science undergraduate degree and is geared toward people looking for a broader understanding of AI, Edmunds says. This is someone who needs to understand artificial intelligence, but isnt necessarily trying to push the envelope of whats trying to be done, she says. Instead, its about advancing how machines are being used and how they can be applied.

In the MSAI program, students learn a comprehensive framework of theory and practice. It focuses on both the foundational knowledge needed to explore key contextual areas and the complex technical applications of AI systems.

This program incorporates data science, robotics, and ML, which enable students to pursue a holistic and interdisciplinary course of study while preparing for a position in research, operations, software or hardware development, or a doctoral degree.

This program takes people from different backgrounds and gives them enough information to be able to talk with a team whos responsible for the more technical artificial intelligence responsibilities, Edmunds says. They dont need to know the nuts and bolts, but theyll leave with enough to know the right questions to ask and make sure theyre being responsible with the technology.

The MSCS with a specialization in artificial intelligence, on the other hand, is designed for people who are, or want to become a software engineer, computer science developer, or computer science researcher in which their focus is on creating new applications for algorithms, for example.

This program is designed for students with a background in computer science and includes courses on robotic science and systems, natural language processing, machine learning, and special topics in artificial intelligence.

AI and ML are going to be how we solve some of the largest problems. Were very focused on making sure that everyone can get access to those skills because thats how were going to create a better world.

To learn more about how a graduate degree can accelerate your career in artificial intelligence, explore our MS in AI and MS in Computer Science program pages, or download the free guide below.

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Artificial Intelligence vs. Machine Learning: Whats the ...

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