Daily Archives: November 28, 2021

Hear from CIOs, CTOs, and other C-level and senior execs on data and AI strategies at the Future of Work Summit this January 12, 2022. Learn more

Its not often the world of semiconductors is turned on its head. Its clear that a similar transformation is occurring as a superabundance of start-ups takes on the challenge of low-power neural nets.

These start-ups are trying to move neural network-based machine learning from the cloud data center to embedded systems in the field to whats now called the edge. Making chips work in this new world will require new ways of setting up neurals, designing memory paths, and compiling to hardware.

Establishing this new formula will challenge the brightest heads in electrical engineering. But the push has begun for edge AI. Its spawned myriad startups, including Axelera.AI, Deep Vision, EdgeQ, Hailo, Sima.ai, and many more.

Driving this, according to analyst firm ABI Research, is the need for local data processing, low latency, and avoidance of repeated calls to AI chips back on the cloud. The firm also cites better data privacy as an impetus. Its all seen as an opening for upstarts in an edge AI chipset market that ABI estimates will grow to $28 billion in 2026, for a compound annual growth rate (CAGR) of 28.4% from 2021 to 2026.

That growth will require designs that move beyond bellwether AI apps, like those that recognize images of cats and dogs, created in power-rich cloud data centers. That quest to expand use cases should bring pause to optimists.

Making the chips is one thing, but getting them to work across many different neural network types is another. We are not there yet, said Marian Verhelst, a circuits and systems researcher at Katholieke Universiteit Leuven and the Imec tech hub in Belgium, as well as a member of the TinyML Foundation, who spoke with VentureBeat.

Still, its a really cool time to be active in this new domain, adds Verhelst, who is also an advisor to Netherlands-based Axelera.AI. The company recently gained $12 million in seed funding from security infrastructure provider Bitfury to pursue Edge AI chips.

What matters when it comes to designing this new chip generation? Chip designers and their customers alike now need to explore the question. In an interview, Verhelst outlined the pressing points as she saw them:

These matters drive design decisions at Axelera AI. The company is preparing to go to market with an accelerator chip centered around analog in-memory processing, transformer neural nets, and data flow architecture while consuming less than 10 watts.

We put together the in-memory computing, which is a new paradigm in technology, and we merge this with a data flow architecture, which gives a lot of flexibility in a small footprint, with small power consumption, said Axelera cofounder and CEO Fabrizio Del Maffeo, who emphasized that this is an accelerator that can work with an agnostic assortment of CPUs.

Del Maffeo cites vision systems, smart cities, manufacturing, drones, and retail as targets for Edge AI efforts.

The competition to forge a solution in edge AI is tough, but entrepreneurs like Del Maffeo and engineers like Verhelst will enthusiastically accept the challenge.

Its a very interesting time for hardware, chips, designers, and startups, Verhelst said. For the first time in a couple of decades, hardware really starts to be at the center of attention again.

No doubt, its interesting to be there when a new IC architecture is born.

Read more from the original source:

The shape of edge AI to come - VentureBeat

Hear from CIOs, CTOs, and other C-level and senior execs on data and AI strategies at the Future of Work Summit this January 12, 2022. Learn more

AI thrives on data. The more data it can access, and the more accurate and contextual that data is, the better the results will be.

The problem is that the data volumes currently being generated by the global digital footprint are so vast that it would take literally millions, if not billions, of data scientists to crunch it all and it still would not happen fast enough to make a meaningful impact on AI-driven processes.

This is why many organizations are turning to AI to help scrub the data that is needed by AI to function properly.

According to Dells 2021 Global Data Protection Index, the average enterprise is now managing ten times more data compared to five years ago, with the global load skyrocketing from just 1.45 petabytes in 2016 to 14.6 petabytes today. With data being generated in the datacenter, the cloud, the edge, and on connected devices around the world, we can expect this upward trend to continue well into the future.

In this environment, any organization that isnt leveraging data to its full potential is literally throwing money out the window. So going forward, the question is not whether to integrate AI into data management solutions, but how.

AI brings unique capabilities to each step of the data management process, not just by virtue of its capability to sift through massive volumes looking for salient bits and bytes, but by the way it can adapt to changing environments and shifting data flows. For instance, according to David Mariani, founder of, and chief technology officer at AtScale, just in the area of data preparation, AI can automate key functions like matching, tagging, joining, and annotating. From there, it is adept at checking data quality and improving integrity before scanning volumes to identify trends and patterns that otherwise would go unnoticed. All of this is particularly useful when the data is unstructured.

One of the most data-intensive industries is health care, with medical research generating a good share of the load. Small wonder, then, that clinical research organizations (CROs) are at the forefront of AI-driven data management, according to Anju Life Sciences Software. For one thing, its important that data sets are not overlooked or simply discarded, since doing so can throw off the results of extremely important research.

Machine learning is already proving its worth in optimizing data collection and management, often preserving the validity of data sets that would normally be rejected due to collection errors or faulty documentation. This, in turn, produces greater insight into the results of trial efforts and drives greater ROI for the entire process.

Still, many organizations are just getting their new master data management (MDM) suites up and running, making it unlikely they will replace them with new intelligent versions any time soon. Fortunately, they dont have to. According to Open Logic Systems, new classes of intelligent MDM boosters are hitting the channel, giving organizations the ability to integrate AI into existing platforms to support everything from data creation and analysis to process automation, rules enforcement, and workflow integration. Many of these tasks are trivial and repetitive, which frees up data managers time for higher-level analysis and interpretation.

This trend toward deploying AI to manage the data it needs to perform other duties in the digital enterprise will change the nature of work for data scientists and other knowledge workers. People will no longer be tasked with doing the work they do now and instead will focus on monitoring the results of AI-driven processes and then making changes should they veer from defined objectives.

More than anything, however, AI-driven data management will speed up the pace of business dramatically. Data is king in the digital universe, and kings dont like to wait.

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AI will soon oversee its own data management - VentureBeat

SINGAPORE - Media OutReach - 29 November 2021 - Established online learning platform Superstar Teacher is partnering with AI-centred EdTech startup Cerebry to launch a new Artificial Intelligence (AI) Learning Mode to complement their existing library of online lessons. Students can now benefit from tailored questions catered to enhancing their strengths and improving on their shortcomings thanks to the help of AI technology.

Superstar Teacher's online course library is built around topical concepts found in MOE syllabuses and commonly tested exam questions. Questions generated through this new Learning Mode will complement existing lessons while ensuring that students have access to a variety of challenging exercises.

Individualised Learning Powered by Artificial Intelligence

The questions (and solutions) generated by Superstar Teacher's AI Learning Mode are automatically generated on the fly, building upon the child's performance in previous lessons. This means that no two students will receive the same question from the AI system. Additionally, the endless possibility of different questions keeps a student engaged and away from boredom. The system will likewise generate intuitive hints and step-by-step solutions corresponding to the question posed to ensure that the student fully understands the process of getting to the correct answer.

Catering to the uniqueness of each child's learning needs, the AI system has the functionality to create personalised study schedules using diagnostic testing and data-driven insights, focusing on concepts the student appears to be weaker at to remedy all knowledge gaps and attain subject mastery.

Efficient and Engaging Revision Plans

If the AI system deems that the child does not fully understand a particular concept, they will be brought back to the specific part of the lesson covering this topic in the online course library. From there, the child will be equipped to review their knowledge in particular areas they may have trouble comprehending.

Unlike a classroom setting where it would be impossible for a teacher to create a personalised and highly precise study plan for every student, the AI Learning Mode's real-time feedback enables individual students to work on their knowledge gaps and efficiently target areas of uncertainty with relevant practices. This also ensures that time will be spent effectively. After revision, they will be prompted to return to the AI Learning mode and complete questions to test their understanding. Students will only be able to progress to higher-level topics once they have displayed mastery in foundational topics.

AI-Human Synergy in Optimising Learning

With the advancement of AI technologies, there are growing concerns and fears that artificial intelligence will eventually replace human educators. However, Superstar Teacher recognises the importance of the interactive element in human teaching and aims to provide a holistic learning environment driven by both AI learning and human experts. This synergy between self-directed AI-enhanced learning and on-demand feedback from qualified mentors will optimise the learning experience for students.

Interested parents can sign their child up for a free trial to experience these features.

Conceptualised in 2011, Superstar Teacher is committed to creating an excellent learning experience for students through quality strategy-based teaching aligned with Singapore's MOE syllabuses. Its online learning platform is equipped with innovative features and aims at keeping students engaged in the pursuit for academic excellence. For more information, visit http://www.superstarteacher.com.sg.

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Superstar Teacher Uses the Latest in AI to Bring Online Learning to the Next Level in the Age of EdTech - Taiwan News

After Clearview AI scraped billions of photos from the public web from websites including Instagram, Venmo and LinkedIn to create a facial recognition tool for law enforcement authorities, many concerns were raised about the company and its norm-breaking tool. Beyond the privacy implications and legality of what Clearview AI had done, there were questions about whether the tool worked as advertised: Could the company actually find one particular persons face out of a database of billions?

Clearview AIs app was in the hands of law enforcement agencies for years before its accuracy was tested by an impartial third party. Now, after two rounds of federal testing in the last month, the accuracy of the tool is no longer a prime concern.

In results announced on Monday, Clearview, which is based in New York, placed among the top 10 out of nearly 100 facial recognition vendors in a federal test intended to reveal which tools are best at finding the right face while looking through photos of millions of people. Clearview performed less well in another version of the test, which simulates using facial recognition for providing access to buildings, such as verifying that someone is an employee.

Were pleased, said Clearviews chief executive, Hoan Ton-That. It reflects our actual-use case.

The company also performed well last month in a test called a one-to-one test of its ability to match two different photos of the same person, simulating the facial verification that people use to unlock their smartphones.

The positive results have been a shot in the arm for the sales team, Mr. Ton-That said.

The National Institute of Standards and Technology has been administering Face Recognition Vendor Tests for two decades. Since those tests began, the report notes, face recognition has undergone an industrial revolution, with algorithms increasingly tolerant of poorly illuminated and other low-quality images, and poorly posed subjects.

Clearview made an impressive debut on the charts for investigative, or one-to-many, searches, but the top performers were SenseTime, a Chinese company, and Cubox, from South Korea. In 2019, the Commerce Department blacklisted SenseTime and 27 other Chinese entities because their products were implicated in Chinas campaign against Uyghurs and other Muslim minorities. Axios has reported that the designation was later changed to Beijing SenseTime, limiting the effects of the blacklisting.

Accuracy aside, questions remain about the legality of Clearviews tool. The authorities in Canada and in Australia have said Clearview broke their laws by failing to get the consent of citizens whose photos are included in the database, and the company is fighting lawsuits over privacy in Illinois and Vermont.

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Clearview AI Does Well in Another Round of Facial Recognition Accuracy Tests - The New York Times

Artificial intelligence has made great advances in many industries since its inception. While talking about the ever-changing travel industry is also taking advantage of AI to transform the way it operates. At present, travel companies highly leverage AI-powered tools and solutions for diverse processes from travel planning to landing at the destination. AI has been incorporated into many areas of the travel and hospitality industry making the lives of travellers across the globe easier.

AI Assistants for Booking: AI assistants and Intelligent chatbots have taken over the place of travel agents allowing travellers to book their flights and accommodation using the internet. These AI chatbots are deployed in social media sites to offer users a more personalized booking experience. The travel reservation giants such as Skyscanner, Booking.com and Expedia are using such operations.Robots for Customer Services: Robots are used to filter customer services in the travel industry, by eliminating the need for human agents. They are being used to know the information or try to find gates in a busy terminal. Hotels are also using robots for this very reason to assist the customers. The latest report also says that it is soon expected to replace robots for the checking process by 2030.AI Forecasting: These days the AI technology can help you in finding the best price at the right time and also provides a heads up on the fight prices too. Some giant companies have also made this feature available to their users. Hopper is one such example that can be used by travellers to avail this opportunity.

Identify Insights: As huge amounts of data are generated every single second, there is a need for travel companies to intercept this data to understand and gain insights. And so, most travel companies are leveraging AI to sort through these vast data sets accurately, which is otherwise a daunting task for human beings.

Sentiment Analysis: Social media is booming like anything. Airlines and hotels are leveraging sentiment analysis combined with AI to identify the sentiment of travellers through social media and how it relates to the journey of the travellers. This technology is being used to deliver a better customer experience to the travel partners.Room Mapping: Most of the hotels are using AI for room mapping to track dynamic prices for the same room across multiple suppliers. AI can also help in predicting dynamic prices for a specific room, which can get a precise idea for when and how long the prices are likely to remain the cheapest as they can.

Smart Baggage Handling: Airports handle millions of bags each year, so most airports are leveraging AI for handling airport baggage systems. They can also be automated with robotics and AI in the future by handling the luggage smartly. They have already implemented AI solutions in some of the airports with pilot projects.

Personalized Travel Planning: Travel planning is likely to transform with an increase in mobile applications that can provide personalized end-to-end travel planning. These applications may include new capabilities such as tracking passengers health by integrating with wearable technologies and recommending safe travel zones. AI can provide better customer satisfaction for travel planning.

Augmented Reality Apps: Artificial Intelligence led to AR which can enhance peoples perceptions of the environment when viewed through dedicated devices or smartphones. Most of the travellers step towards AR-enabled travel apps seems like a no-brainer. They all use AR apps to improve the overall visitor experience with interactivity and instantly accessible information.Online Reputation Management: With the help of artificial intelligence, companies can now easily monitor their customer reviews. AI enables the companies to know about social media comments, services and other mentions about the brand. Artificial intelligence can view customer reviews and comments that would not be possible for a human being. By using this technology, hotels and airlines can easily find out the negative feedback of the customers and respond accordingly.

The Future of AI in the Travel IndustryAI Technology in the travel industry has steadily plunged and transformed the way we travel today. There are many potential ways passengers and travel companies can utilize AI to make the journey an efficient and smooth one while maintaining customer satisfaction at a higher level. From a consumer viewpoint, AI makes it easier for users to find the relevant information, make informed decisions, customize deals and give better customer experiences. And from the business point of view, it can improve the efficiency and reduce the cost of the travel industry.

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Analytics Insight is an influential platform dedicated to insights, trends, and opinions from the world of data-driven technologies. It monitors developments, recognition, and achievements made by Artificial Intelligence, Big Data and Analytics companies across the globe.

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10 AI Innovations that are Revolutionizing the Travel Industry - Analytics Insight

By Harvard John A. Paulson School of Engineering and Applied SciencesNovember 28, 2021

Lily Xu. Credit: Eliza Grinnell/Harvard SEAS

Lily Xu knew from a young age how much the environment and conservation mattered to her.

By 9 years old, shed already decided to eat vegetarian because, as she put it, I didnt want to hurt animals.

Xu grew up believing her passions would always be separate from her professional interest in computer science. Then she became a graduate student in Milind Tambes Teamcore Lab, and everything changed.

Xu is now doing award-winning research into using machine learning and artificial intelligence to help conservation and anti-poaching efforts around the world. Her recent paper, Learning, Optimization, and Planning Under Uncertainty for Wildlife Conservation, won the 2021 INFORMS Doing Good with Good OR Student Paper Competition.

From our earliest conversations, it was crystal clear that Lily was very passionate about sustainability, conservation, and the environment, said Tambe, the Gordon McKay Professor of Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS). This was also the reason our wavelengths matched and I went out of my way to recruit her and ensure she joined my group.

In the Teamcore Lab, Xu helped develop Protection Assistant for Wildlife Security (PAWS), an artificial intelligence system that interfaces with a database used by park rangers to record observations of illegal poaching and predict which areas are likely to be poaching hotspots. The system makes it easier for rangers to choose the best locations to patrol.

Lily Xu poses at the entrance to Srepok Wildlife Sanctuary in Cambodia. Credit: Lily Xu

In 2019, Xu and the Teamcore Lab partnered with the Srepok Wildlife Sanctuary in Cambodia to test the efficacy of PAWS. At the time, the sanctuary only had 72 rangers to patrol an area slightly larger than the state of Rhode Island.

Our work with Cambodia was the most intensive collaboration with a park that weve had, said Xu. We had several months of meetings, and our interactions with them and the feedback they were giving us about the process really shaped the design of our algorithms.

Xu played a lead role in implementing field tests of the PAWS program. Through Tambe, Xu and her lab mates, Srepoks rangers greatly increased the number of poachers snares they removed throughout the sanctuary.

Lily has led and taken PAWS from a small research concept to a globally impactful research effort leading to removal of thousands of lethal animal snares, saving endangered wildlife globally, said Tambe. Lily has led a global effort that has made the PAWS software available worldwide to hundreds of national parks. This is true global impact, aiming to save endangered wildlife around the world.

Lily Xu patrols Srepok Wildlife Sanctuary in Cambodia. Credit: Lily Xu

Xu has always loved nature, but didnt get to experience much of it while growing up in the Maryland suburbs of Washington, D.C. Once she got to Dartmouth College as an undergraduate in 2014, she finally got to immerse herself in the outdoors.

I went hiking and camping for the first time as part of my freshman orientation trip, just absolutely fell in love with it, and then spent as much time as I could outdoors, she said. That made me even more attuned to how precious the natural environment is, and how much I care about doing my part to preserve it.

She eventually began to help organize Dartmouths first-year trip and took on leadership roles with the schools sophomore trip and canoe club. Xu didnt want to just experience nature, she wanted others to care about it too.

Thats continued at Harvard, where shes mentored four students since the summer of 2020, and been part of several mentorship teams.

I care a lot about mentorship in all capacities, whether thats bringing people out of their comfort zone, encouraging them to explore the outdoors and realize that this is a place for them, Xu said. The outdoors community is traditionally wealthy and traditionally white. Im neither of those things, and I really want to encourage other people and show them that this can be their space too. Similarly, from a computer science standpoint, this is a field that is traditionally male-dominated, and especially in AI research, its traditionally people in the western world.

Xu has published multiple award-winning publications through her work on PAWS. A paper presented at the 35th Association for the Advancement of Artificial Intelligence, Dual-Mandate Patrols: Multi-Armed Bandits for Green Security, was named a Best Paper Award Runner-Up as a top-six paper out of nearly 1,700 accepted papers, while another publication, Enhancing Poaching Predictions for Under-Resourced Wildlife Conservation Parks Using Remote Sensing Imagery, won the Best Lightning Paper Award at the Machine Learning for Development Workshop at the 34th Conference on Neural Information Processing Systems in 2020.

Xu is working to address those disparities as a member of Mechanism Design for Social Good (MD4SG), a multi-school, multi-disciplinary research initiative that organizes working groups and colloquium series to address the needs of underserved and marginalized communities all over the world. Xu joined MD4SG in 2020 as co-organizer for the groups environmental working group, and this past March became a co-organizer for the entire organization.

I thought, Oh this sounds like a phenomenal opportunity, because I dont really know of a strong community of computational researchers who are working in environmental challenges, and I would love to help foster a community, Xu said. Our working group, for example, has really been able to bring in people from all around the world.

Shes fantastic to work with in all of these areas, said Bryan Wilder, PhD 21, a former Teamcore lab member and member of the MD4SG leadership team. She has the combination of being incredibly engaged and energetic and really making things happen, while also just being a kind person to work with.

For Xu, research is about more than just publishing its all about building relationships and fostering community engagement.

We are researchers that are not just trying to get your data sets, publish a paper and then just walk away, said Xu. We are here for the long run. We are committed. We want to achieve conservation results as much as we want to achieve academic publication.

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Computer Conservation: Lily Xu Uses Artificial Intelligence To Stop Poaching Around the World - SciTechDaily