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ProBeat: The question of cloud AI or edge AI is far from settled

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This week, I did a deep dive into Google Meet’s noise cancellation, a couple months after detailing Microsoft Teams’ noise suppression. Both use supervised learning. Both try to filter out typing, vacuum cleaners, and rustling bags while keeping speech, singing, and laughter. Sure, Google Meet cancels out musical instruments while Microsoft Teams keeps them, but other than that they’re nearly identical. At least it looks like they are, until you look under the hood.

The timing is no coincidence either — collaboration and video conferencing tools have never been more important than during the age of the coronavirus, when millions have to learn and work from home. Google and Microsoft are putting their machine learning chops to the test in the hopes of one-upping Zoom and crushing Slack. Google Meet and Microsoft Teams use AI to remove background noise in real time so you hear only speech on a meeting call. And yet what struck me after I interviewed their respective product leads is how differently the companies are approaching the same problem.

Here’s the simple version: Google put its machine learning model in the cloud, while Microsoft put its machine learning model on the edge. But there’s more to it than that — let me quote the product leads directly.

Here is Serge Lachapelle, G Suite director of product management:

Our job has always been passing through the cloud as quickly as possible. But now with these TensorFlow processors, and basically the way that our infrastructure is built, we discovered that we could do media manipulation in real time and add sometimes only around 20 milliseconds of delay. So that’s the road we took.

Here is Robert Aichner, Microsoft Teams group program manager:

A lot of the machine learning happens in the cloud. So for speech recognition, for example, you speak into the microphone, that’s sent to the cloud. The cloud has huge compute, and then you run these large models to recognize your speech. For us, since it’s real-time communication, I need to process every frame. Let’s say it’s 10 or 20 millisecond frames. I need to now process that within that time so that I can send that immediately to you. I can’t send it to the cloud, wait for some noise suppression, and send it back.

That latency question also leads to a question around cost. Every additional network hop adds latency and doing a lot of server processing for each call increases cost.

Google’s Lachapelle, on cost:

There’s a cost associated with it. Absolutely. But in our modeling, we felt that this just moves the needle so much that this is something we need to do. And it’s a feature that we will be bringing at first to our paying G Suite customers. As we see how much it’s being used and we continue to improve it, hopefully we’ll be able to bring it to a larger and larger group of users.

Microsoft’s Aichner, on cost:

You want to make sure that you push as much of the compute to the endpoint of the user because there isn’t really any cost involved in that. You already have your laptop or your PC or your mobile phone, so now let’s do some additional processing. As long as you’re not overloading the CPU, that should be fine.

But then there are other trade-offs to consider.

Google’s Lachapelle, on speed:

Doing this without slowing things down is so important because that’s basically what a big chunk of our team does — try to optimize everything for speed, all the time. We can’t introduce features that slow things down. And so I would say that just optimizing the code so that it becomes as fast as possible is probably more than half of the work. More than creating the model, more than the whole machine learning part. It’s just like optimize, optimize, optimize. That’s been the hardest hurdle.

Microsoft’s Aichner, on battery life:

Yeah, battery life, we are obviously paying attention to that too. We don’t want you now to have much lower battery life just because we added some noise suppression. That’s definitely another requirement we have when we are shipping. We need to make sure that we are not regressing there.

At first glance, these different approaches make sense. It’s right there in the companies’ respective DNA. Google was born in the internet age, while Microsoft pioneered the software era. Microsoft is traditionally about software installed locally, while Google is all about apps hosted in the cloud. This is Microsoft Office versus G Suite in a nutshell.

Still, it’s never that simple. Sure, Office dwarfs G Suite, but Microsoft Azure is more successful than Google Cloud. Meanwhile, Google Chrome won so thoroughly that Edge is now based on Chromium.

But I digress. In building out noise filtering for their respective video calling solutions, Google and Microsoft took decisively different approaches. Google went with the cloud to bring the same experience to everyone, cost be damned. Microsoft went with the edge to bring the best experience to everyone, complexity be damned.

Both Lachapelle and Aichner acknowledge to me that they may have to change their approach based on how the rollout of each feature goes. It’s too early to say which solution is superior, or whether there will even be a winner. If, however, one of these companies backpedals, there will be a clear loser: either the cloud or the edge.

ProBeat is a column in which Emil rants about whatever crosses him that week.

Source: http://feedproxy.google.com/~r/venturebeat/SZYF/~3/KzxFMec9Kcs/

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Aite survey: Financial institutions will invest more to automate loan process

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Financial institutions plan to increase their spend on automations and collections management solutions for their loan processes. Fresh results on consumer lending practice from research and advisory firm Aite Group indicate lenders plan to invest more heavily in their collections processes, said Leslie Parrish, senior analyst for the Aite Group’s consumer lending practice. Parrish shared […]

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Source: https://bankautomationnews.com/allposts/lending/aite-survey-financial-institutions-will-invest-more-to-automate-loan-process/

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Facial recognition, other ‘risky’ AI set for constraints in EU

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Facial recognition and other high-risk artificial intelligence applications will face strict constraints under new rules unveiled by the European Union that threaten hefty fines for companies that don’t comply.

The European Commission, the bloc’s executive body, proposed measures on Wednesday that would ban certain AI applications in the EU, including those that exploit vulnerable groups, deploy subliminal techniques or score people’s social behavior.

The use of facial recognition and other real-time remote biometric identification systems by law enforcement would also be prohibited, unless used to prevent a terror attack, find missing children or tackle other public security emergencies.

Facial recognition is a particularly controversial form of AI. Civil liberties groups warn of the dangers of discrimination or mistaken identities when law enforcement uses the technology, which sometimes misidentifies women and people with darker skin tones. Digital rights group EDRI has warned against loopholes for public security exceptions use of the technology.

Other high-risk applications that could endanger people’s safety or legal status—such as self-driving cars, employment or asylum decisions — would have to undergo checks of their systems before deployment and face other strict obligations.

The measures are the latest attempt by the bloc to leverage the power of its vast, developed market to set global standards that companies around the world are forced to follow, much like with its General Data Protection Regulation.

The U.S. and China are home to the biggest commercial AI companies — Google and Microsoft Corp., Beijing-based Baidu, and Shenzhen-based Tencent — but if they want to sell to Europe’s consumers or businesses, they may be forced to overhaul operations.

Key Points:

  • Fines of 6% of revenue are foreseen for companies that don’t comply with bans or data requirements
  • Smaller fines are foreseen for companies that don’t comply with other requirements spelled out in the new rules
  • Legislation applies both to developers and users of high-risk AI systems
  • Providers of risky AI must subject it to a conformity assessment before deployment
  • Other obligations for high-risk AI includes use of high quality datasets, ensuring traceability of results, and human oversight to minimize risk
  • The criteria for ‘high-risk’ applications includes intended purpose, the number of potentially affected people, and the irreversibility of harm
  • AI applications with minimal risk such as AI-enabled video games or spam filters are not subject to the new rules
  • National market surveillance authorities will enforce the new rules
  • EU to establish European board of regulators to ensure harmonized enforcement of regulation across Europe
  • Rules would still need approval by the European Parliament and the bloc’s member states before becoming law, a process that can take years

—Natalia Drozdiak (Bloomberg Mercury)

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Source: https://bankautomationnews.com/allposts/comp-reg/facial-recognition-other-risky-ai-set-for-constraints-in-eu/

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Prioritizing Artificial Intelligence and Machine Learning in a Pandemic

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AI and ML
Illustration: © IoT For All

Artificial Intelligence (AI) and Machine Learning (ML) give companies the one thing humans can’t – scalability. Over time, humans limit a businesses’ ability to scale; they can only work so many hours at a given efficiency. On the other hand, AI and ML can work around the clock with the sole focus on a given project. As organizations navigate through COVID-19’s impact and the future of a remote workforce, scalability and efficiency can be the key to an organization’s successful recovery.

Implementation Challenges

The benefits of AI and ML don’t come without their own challenges; however, the top challenges are a lack of skills and time for proper implementation. In July, Deloitte found in a survey that 69% of respondents said the skills gap for AI implementation ranged from moderate to major to extreme. Simultaneously, many companies overlook the investment it takes to build the processes and infrastructure needed for successfully training, testing, deploying, and maintaining AI and ML in their enterprise.

Such challenges often cause companies to de-prioritize AI and ML projects, especially in times of uncertainty. That has been increasingly obvious throughout the COVID-19 pandemic. But while some organizations have drawn back on their efforts, the current global state demands the greater need for AI and ML to support critical business processes. This is especially true today given the growing remote workforce, considerations for returning to the workplace and work happening in silos worldwide.

Though challenging, it is not impossible to properly implement AI and ML. In this evolving COVID-influenced business landscape, four steps are key to effectively implementing a strong AI and ML system that helps streamline critical business processes despite uncertainty and limited resources.

Identify the Problem to Be Solved

Some companies mistakenly view AI and ML projects as a ‘silver bullet’ to solve all their problems. This often results in overinflated expectations, an unfocused approach, and unsatisfactory results. Instead, companies should identify those specific problems that will have the biggest impact from implementing AI and ML solutions and be hyper-focused on solving those problems.

Select Your Data

The second step in creating a strong AI and ML algorithm is to select the source data that your algorithm will be training on. There are two main options: training on your own data or training on a larger scale data set. Based on experience, training your algorithm on your own data puts you at a disadvantage. By training on a larger scale data set, the likelihood of success increases because your data is more representative and varied. Through advanced concepts such as transfer learning, companies can use semi-trained models based on larger data sets and then train the “last mile” using their own specific content unique to their business.

Clean House

The standby rules of data management apply here – garbage in, garbage out. Ultimately, the quality and accuracy of machine learning models depend on being representative. AI and ML – fed with the right data – can streamline operations and increase the benefit of companies’ DX and cloud migration journeys.

When you’re kicking off an AI or ML project, the most critical step is to clean up the data that your algorithm will be training on, especially if you’re using your own data or models.

Make Room for Training

AI and ML are all about probability. When you ask it a question, for example, “Is this a cat?,” the results you receive are the algorithm saying, “Out of the three buckets I was trained on, the likelihood of this image being a cat is .91, the likelihood of this image being a dog is .72 and the likelihood of this image being a bird is .32.”

This is why training on varied data is so important. If your training data only includes images of cats, dogs, and birds and you ask the algorithm to analyze the picture of a crocodile, it will only respond based on the buckets it’s been trained on – cats, dogs, and birds.

If you’ve properly selected and cleaned your data, training should be an easy last step, but it’s also an opportunity to go back to the first two steps and further refine based on your training.

The front end of training an AI and ML algorithm can be time-intensive, but following these four steps can make it easier to achieve significant outcomes. Across industries, AI and ML can quickly show ROI. For example, in the insurance industry, AI and ML can help insurers quickly search contracts, so employees aren’t sifting through contracts and repositories around the globe to answer simple questions. This means time efficiencies for an industry that COVID-19 has heavily impacted.

Even better, working with a SaaS provider with experience in your industry can make this process much easier and less costly. SaaS platforms allow companies to take advantage of having all of the infrastructure, security, and pre-trained models in place to reduce the overall effort and time to value. Many platforms allow users to uptrain the predefined models with unique customer data, reducing the training effort needed for model creation. Companies can then focus on integration with their ecosystem and workflows rather than model creation itself.

Bigger Picture

Overall, businesses can soften the impact of COVID by focusing on the bigger picture with AI and ML. Implementing AI and ML projects increase business productivity despite these times of uncertainty. As we continue on the road to recovery, we need tools like AI and ML to stay focused on the bigger picture, mission-critical tasks.

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Source: https://www.iotforall.com/prioritizing-artificial-intelligence-and-machine-learning-in-a-pandemic

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ProGlove promotes worker well-being with human digital twin technology

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ProGlove, the company behind an ergonomic barcode scanner, has developed new tools for analyzing human processes to build a human digital twin.

“We have always been driven to have our devices narrate the story of what is really happening on the shop floor, so we added process analytics capabilities that allow for time-motion studies, visualization of the shop floor, and more,” ProGlove CEO Andreas Koenig told VentureBeat.

The company’s newest process analytics tools can complement the typical top-down perspective of applications by adding a process-as-seen view to the conventional process-as-wanted view. Most importantly, it can also provide insights that improve well-being.

Koenig said, “We are building an ecosystem that empowers the human worker to make their businesses stronger.”

ProGlove CEO Andreas Koenig

Above: ProGlove CEO Andreas Koenig

Image Credit: ProGlove

The market for barcode scanning is still going strong and is often taken for granted, given how old it is. “You have technologies like RFID that have been celebrated for being the next big thing, and yet their impact thus far hasn’t been anywhere near where most pundits expected it,” Koenig said.

Companies like Zebra, Honeywell, and Datalogic have lasted for decades by building out an ecosystem of tools to address industry needs. “What sets us apart is that we looked beyond the obvious and started with the human worker in mind,” Koenig said.

Not only is the company providing a form factor designed to meet requirements for rugged tools, this shift to analytics could further promote efficiency, quality, and ergonomics on the shop floor.

How a human digital twin works

ProGlove’s cofounders participated in Intel’s Make It Wearable Challenge, with the idea of designing a smart glove for industries. Today, ProGlove’s MARK scanner can collect six-axis motion data, including pitch, yaw, roll, and acceleration, along with timestamps, a step count, and camera data (such as barcode reading speed and the scanner ID).

Koenig’s vision goes beyond selling a product to establish the right balance between businesses’ need for profits and their obligation to ensure worker well-being. Koenig estimates that human hands deliver 70% of added value in factories and on warehouse floors. “There is no doubt that they are your most valuable resource that needs protection. Even more so since we are way more likely to experience a shortage of human workers in the warehouses across the world than having them replaced by robots, automation, or AI.”

ProGlove Insight contextualizes the collected data and lets users compare workstations and measure the workload and effort necessary to complete the tasks. Users can also visualize their shop floor, look at heatmaps, and identify best practices or efficiency blockers. After a recent smart factory lab experiment with users, DPD and Asics realized efficiency gains by as much as 20%, Koenig said.

ProGlove’s vision of the human digital twin is built on three pillars: a digital representation of onsite workers, a visualization of the shop floor, and an industrial process engineer. “The human digital twin is all about striking the right balance between businesses’ needs for profitability, efficiency, and worker well-being,” Koenig said. At the same time, it is important that the human digital twin complies with data privacy regulations and provides transparency to frontline workers around what data is being transmitted.

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Source: https://venturebeat.com/2021/04/21/proglove-promotes-worker-well-being-with-human-digital-twin-technology/

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