November 24, 2020

AI/ML

DailyHum News
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Vision AI will be the next worldwide web
AI/ML

The web may not be the largest thing to run on the internet (these days it seems like Zoom is) but it was the most transformational until mobile apps came along. You can follow the waves by developer interest: in the 2000s everyone was learning HTML and making a website. In the 2010s everyone was learning to develop mobile apps. In the 2020s all the developers are going to build Vision AI. And for good reason.Where the web had its impact was by digitizing manual paper-based processes. Rather than receive a bank statement in the mail you could view it on the web. Rather than mail in a check, you could pay on the web. Rather than fax in a trade authorization, you could validate it on the web.This extended to internal enterprise processes, from product configuration to employee surveys, and to B2B processes, from catalog updates to credit reporting. All the information was now digital, thanks to the portal we call the web, and could be acted upon digitally. When mobile apps came along, the groundwork of digitized information was there to make that data available in the palm of our hands.
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Sphero spinout Company Six launches throwable, video-streaming wheeled drone for first responders
AI/ML

In May, Sphero, the decade-old Colorado-based company best known for its programmable robots, announced Company Six (CO6), a spinoff focused on commercializing intelligence robots and AI-based apps for military, EMT, and fire personnel and others who work in challenging situations. Mum’s been the word since on what exactly that might entail, but today, CO6 took the wraps off of its ReadySight, a one-pound, throwable robot built for “dangerous and difficult” jobs.Robots are ripe for first responder scenarios, as novel research and commercial products continue to demonstrate. Machines like those from RedZone can autonomously inspect sewage pipes for corrosion, deformation, and debris in order to prevent leaks that could pose health hazards. And drones like the newly unveiled DJI M300 RTK and Parrot Anafi Thermal have been tapped by companies like AT&T and government agencies for maintenance inspections and assistance in disaster zones. CO6 appears poised to carve out a niche in this market, which was estimated to be worth in excess of $3.7 billion.
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Gatik raises $22.5 million for autonomous short-haul delivery trucks
AI/ML

Gatik, a startup developing an autonomous vehicle stack for B2B short-haul logistics, today closed a $22.5 million series A round. The company also announced it will bring a fleet of self-driving vans to Canada as part of a deal with Loblaw, the country’s largest retailer with over 200,000 employees.Some experts predict the pandemic will hasten adoption of autonomous vehicles for delivery. Self-driving cars, vans, and trucks promise to minimize the risk of spreading disease by limiting driver contact. This is particularly true with regard to short-haul freight, which is experiencing a spike in volume during the outbreak. The producer price index for local truckload carriage jumped 20.4% from July to August, according to the U.S. Bureau of Labor Statistics, most likely propelled by demand for short-haul distribution from warehouses and distribution centers to ecommerce fulfillment centers and stores.
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An antidote to “fast fashion”
AI/ML

The clothing rental service Armoire helps customers sustainably maintain a fresh ward-robe.
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How Hasty uses automation and rapid feedback to train AI models and improve annotation
AI/ML

Computer vision is playing an increasingly pivotal role across industry sectors, from tracking progress on construction sites to deploying smart barcode scanning in warehouses. But training the underlying AI model to accurately identify images can be a slow, resource-intensive endeavor that isn’t guaranteed to produce results. Fledgling German startup Hasty wants to help with the promise of “next-gen” tools that expedite the entire model training process for annotating images.Hasty, which was founded out of Berlin in 2019, today announced it has raised $3.7 million in a seed round led by Shasta Ventures. The Silicon Valley VC firm has a number of notable exits to its name, including Nest (acquired by Google), Eero (acquired by Amazon), and Zuora (IPO). Other participants in the round include iRobot Ventures and Coparion.
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Percepto raises $45 million for robots that inspect critical infrastructure
AI/ML

Autonomous inspection solutions company Percepto today announced a $45 million round. The funds come as Percepto pivots from drone-based products to general robotics-driven inspections incorporating third-party platforms like Boston Dynamics’ Spot.According to a report from Technavio, the inspection robot market has the potential to grow to $3.72 billion between 2020 and 2024, bolstered by industries spanning energy, oil and gas, and mining. Beyond their cost-effectiveness, robots have the ability to travel where humans can’t — either for safety or physical reasons. Machines like Spot can also be equipped with specialized hardware that delivers more detailed, consistent analytics and insights than an inspector could.
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Glue raises $8 million to automate customer loyalty programs
AI/ML

Loyalty automation platform Glue today raked in $8 million in series A funding from private investors led by Unicorn Technologies. The startup says that the proceeds will be put toward nudging local businesses to adopt loyalty programs.Retail has taken a major hit during the pandemic. Total sales are expected to hit 5.7% from 2019, nearly 12% below eMarketer’s pre-pandemic estimate of $26 trillion. Some data suggests that loyalty programs could help lessen future blows. According to Accenture, loyalty program membership in the U.S. grew at a rate of 26.7% from 2012 to 2014. And one recent survey found that 50% of consumers say their primary reason for joining a loyalty program is to earn rewards on purchases.
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Extreme Gradient Boosting (XGBoost) Ensemble in Python
AI/ML

Extreme Gradient Boosting (XGBoost) is an open-source library that provides an efficient and effective implementation of the gradient boosting algorithm. Although other open-source implementations of the approach existed before XGBoost, the release of XGBoost appeared to unleash the power of the technique and made the applied machine learning community take notice of gradient boosting more […]
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UC Berkeley researchers detect ‘silent speech’ with electrodes and AI
AI/ML

UC Berkeley researchers say they are the first to train AI using using silently mouthed words and sensors that collect muscle activity. Silent speech is detected using electromyography (EMG), with electrodes placed on the face and throat. The model focuses on what researchers call digital voicing to predict words and generate synthetic speech.Researchers believe their method can enable a number of applications for people who are unable to produce audible speech and could support speech detection for AI assistants or other devices that respond to voice commands.“Digitally voicing silent speech has a wide array of potential applications,” the team’s paper reads. “For example, it could be used to create a device analogous to a Bluetooth headset that allows people to carry on phone conversations without disrupting those around them. Such a device could also be useful in settings where the environment is too loud to capture audible speech or where maintaining silence is important.”
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How humans use objects in novel ways to solve problems
AI/ML

What's SSUP? The Sample, Simulate, Update cognitive model developed by MIT researchers learns to use tools like humans do.
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How to Develop a Light Gradient Boosted Machine (LightGBM) Ensemble
AI/ML

Light Gradient Boosted Machine, or LightGBM for short, is an open-source library that provides an efficient and effective implementation of the gradient boosting algorithm. LightGBM extends the gradient boosting algorithm by adding a type of automatic feature selection as well as focusing on boosting examples with larger gradients. This can result in a dramatic speedup […]
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Salesforce-backed AI project SharkEye aims to protect beachgoers
AI/ML

Salesforce is backing an AI project called SharkEye which aims to save the lives of beachgoers from one of the sea’s deadliest predators. Shark attacks are, fortunately, quite rare. However, they do happen and most cases are either fatal or cause life-changing injuries. Just last week, a fatal shark attack in Australia marked the eighth... Read more » The post Salesforce-backed AI project SharkEye aims to protect beachgoers appeared first on AI News.
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This is how we’ll merge with AI
AI/ML

The relationship between humans and AI is something of a dance. We and AI come close together operating collaboratively, then are pushed away by the impossibility, only to stumble but return attracted by the potential. It is perhaps fitting that the dance community is beginning to embrace robots, with AI helping to create new movements and choreography, and with robots sharing the stage with human dancers.The relationship between society and technology is yin and yang, with every massive enhancement accompanied by the potential for danger. AI, for example, offers the promise to end boring, repetitive jobs, enabling us to engage in higher level and more fulfilling tasks. It helps with any number of efficiency efforts, such as fraud detection, and it can even paint masterpiece artworks and compose symphonies. Sam Altman, CEO of OpenAI, hopes AI will unlock human potential and let us focus on the most interesting, most creative, most generative things.
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Salesforce’s Einstein platform is now serving over 80 billion predictions per day
AI/ML

In September 2016, Salesforce launched Einstein, an AI platform to power predictions across all of the company’s cloud-hosted products. Just over four years after Einstein’s debut, Salesforce says the platform is now delivering over 80 billion AI-powered predictions every day, up from 6.5 billion predictions in October 2019.Forrester Research recently wrote that companies “have to rebuild their businesses, not for today, or even next year, but to prepare to compete in an AI-driven future.” Reflecting this changing landscape, IDC expects global spending on AI to more than double to $110 billion in 2024, up from $50 billion in 2020.Salesforce asserts that Einstein is poised to drive a substantial portion of this growth. Einstein’s predictions can include internal and customer service answers for a given use case, like when to engage with a sales lead, how likely an invoice is to be paid, and which products to recommend to bolster sales. For instance, outdoor apparel and lifestyle brand Orvis taps Einstein to develop personalized conversations with its online shoppers. Internet Creations, a business technology and consulting firm, is using Einstein to forecast long- and short-term cash flow during the pandemic. And outdoor apparel retailer Icebreaker is leveraging Einstein to suggest products for new and existing target audiences.
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Training AI algorithms on mostly smiling faces reduces accuracy and introduces bias, according to research
AI/ML

Facial recognition systems are problematic for a number of reasons, not least of which they tend to exhibit prejudice against certain demographic groups and genders. But a new study from researchers affiliated with MIT, the Universitat Oberta de Catalunya in Barcelona, and the Universidad Autonoma de Madrid explores another problematic aspect that’s received less attention so far: bias toward certain facial expressions. The coauthors claim that the impact of expressions on facial recognition systems is “at least” as impactful as wearing a scarf, hat, wig, or glasses, and that facial recognition systems are trained with highly biased datasets in this regard.The study adds to a growing body of evidence that facial recognition is susceptible to harmful, pervasive prejudice. A paper last fall by University of Colorado, Boulder researchers demonstrated that AI from Amazon, Clarifai, Microsoft, and others maintained accuracy rates above 95% for cisgender men and women but misidentified trans men as women 38% of the time. Independent benchmarks of major vendors’ systems by the Gender Shades project and the National Institute of Standards and Technology (NIST) have demonstrated that facial recognition technology exhibits racial and gender bias and have suggested that current facial recognition programs can be wildly inaccurate, misclassifying people upwards of 96% of the time.
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More skin-like, electronic skin that can feel
AI/ML

The challenge for electronic skin, being developed for use in artificial skins or humanlike robots like the humanoids, is to make it feel the temperatures and movements like how human skin feels them as much as possible. So far, there are electronic skins that can detect movement or temperature separately, but none are able to recognize both simultaneously like the human skin.A joint research team consisting of POSTECH professor Unyong Jeong and Dr. Insang You of the Department of Materials Science and Engineering, and Professor Zhenan Bao of Stanford University have together developed the multimodal ion-electronic skin that can measure the temperature and mechanical stimulation at the same time. The research findings, published on November 20th edition of Science, are characterized by making very simple structures through applying special properties of the ion conductors.
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AI that directs drones to film ‘exciting’ shots could lower video production costs
AI/ML

Because of their ability to detect, track, and follow objects of interest while maintaining safe distances, drones have become an important tool for professional and amateur filmmakers alike. This being the case, quadcopters’ camera controls remain difficult to master. Drones might take different paths for the same scenes even if their positions, velocities, and angles are carefully tuned, potentially ruining the consistency of a shot.In search of a solution, Carnegie Mellon, University of Sao Paulo, and Facebook researchers developed a framework that enables users to define drone camera shots working from labels like “exciting,” “enjoyable,” and “establishing.” Using a software simulator, they generated a database of video clips with a diverse set of shot types and then leveraged crowdsourcing and AI to learn the relationship between the labels and certain semantic descriptors.
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Misinformation or artifact: A new way to think about machine learning
AI/ML

They are capable of seemingly sophisticated results, but they can also be fooled in ways that range from relatively harmless -- misidentifying one animal as another -- to potentially deadly if the network guiding a self-driving car misinterprets a stop sign as one indicating it is safe to proceed.A philosopher with the University of Houston suggests in a paper published in Nature Machine Intelligence that common assumptions about the cause behind these supposed malfunctions may be mistaken, information that is crucial for evaluating the reliability of these networks.As machine learning and other forms of artificial intelligence become more embedded in society, used in everything from automated teller machines to cybersecurity systems, Cameron Buckner, associate professor of philosophy at UH, said it is critical to understand the source of apparent failures caused by what researchers call "adversarial examples," when a deep neural network system misjudges images or other data when confronted with information outside the training inputs used to build the network. They're rare and are called "adversarial" because they are often created or discovered by another machine learning network -- a sort of brinksmanship in the machine learning world between more sophisticated methods to create adversarial examples and more sophisticated methods to detect and avoid them.
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