Monthly Archives: August 2021

In brief The case against a man accused of murder has been thrown out by a judge after prosecutors withdrew disputed evidence of an AI-identified gunshot sound.

Michael Williams, 65, who denied any wrongdoing, sat in jail for 11 months awaiting trial for allegedly killing Safarian Herring, 25.

It's said that in May last year, Williams was driving through Chicago one night hoping to buy some cigarettes. Herring waved him down for a ride, and Williams, recognizing the younger man from the neighborhood, let him into his car. Soon after another vehicle pulled up alongside, and someone in a passenger seat took out a gun and shot Herring in the head, Williams told police. Herring's mother said her son, an aspiring chef, had been shot at two weeks earlier at a bus stop.

Herring, who was taken to hospital by Williams, died from the gunshot wound, and Williams ended up being charged with his murder. A key piece of evidence against him came from ShotSpotter, a company that operates microphones spread across US cities including Chicago that, with the aid of machine-learning algorithms, detect and identify gunshot sounds to immediately alert the cops.

Prosecutors said ShotSpotter picked up a gunshot sound where Williams was seen on surveillance camera footage in his car, putting it all forward as proof that Williams shot Herring right there and then. Police did not cite a motive, had no eyewitnesses, and did not find the gun used in the attack. Williams did have a criminal history, though, having served time for attempted murder, robbery, and discharging a firearm when he was younger, and said he had turned his life around significantly since. He was grilled by detectives, and booked.

Crucially, Williams' lawyers public defenders Lisa Boughton and Brendan Max said records showed that ShotSpotter actually initially picked up what sounded like a firework a mile away, and this was later reclassified by ShotSpotter staff to be a gunshot at the intersection where and when Williams was seen on camera. ShotSpotter strongly insisted it had not improperly altered any data to favor the police's case, and said that regardless of the initial real-time alert, its evidence of the gunshot was the result of follow-up forensic analysis, which was submitted to the courts.

After Williams' lawyers asked the judge in the case to carry out an inquiry, the prosecution last month withdrew the ShotSpotter report, and asked for the case to the dismissed on the basis of insufficient evidence, which the judge agreed to. Williams is now a free man again.

I kept trying to figure out, how can they get away with using the technology like that against me, Williams told the Associated Press for an in-depth investigation into the case published this week. Thats not fair.

Startup Kapwing, which built a web application that uses computer-vision algorithms to generate pictures for people, is disappointed netizens used the code to produce NSFW material.

The software employs a combination of VQGAN and CLIP made by researchers at the University of Heidelberg and OpenAI, respectively to turn text prompts into images. This approach was popularised by artist Katherine Crowson in a Google Collab notebook; there's a Twitter account dedicated to showing off this type of computer art.

Kapwing had hoped its implementation of VQGAN and CLIP on the web would be used to make art from users' requests; instead, we're told, it was used to make filth.

Since I work at Kapwing, an online video editor, making an AI art and video generator seemed like a project that would be right up our alley, Eric Lu, co-founder and CTO at Kapwing said.

The problem? When we made it possible for anyone to generate art with artificial intelligence, barely anyone used it to make actual art. Instead, our AI model was forced to make videos for random inputs, trolling queries, and NSFW intents.

Submitted prompts ranged from naked woman to the downright bizarre thong bikini covered in chocolate or gay unicorn at a funeral. The funny thing is, the images made by the AI aren't even that realistic nor sexually explicit. Below is an example output for "naked woman."

Click to enlarge

Is is that the internet just craves NSFW content so much that they will type it anywhere? Or do people have a propensity to try to abuse AI systems?" Lu continued. "Either way, the content outputted must have [been] disappointing to these users, as most of the representations outputted by our models were abstract."

Intel is shuttering its RealSense computer-vision product wing. The business unit's chips, cameras, LiDAR, hardware modules, and software were aimed at things like digital signage, 3D scanning, robotics, and facial-authentication systems.

Now the plug's been pulled, and RealSense boss Sagi Ben Moshe is departing Intel after a decade at the semiconductor goliath.

We are winding down our RealSense business and transitioning our computer vision talent, technology and products to focus on advancing innovative technologies that better support our core businesses and IDM 2.0 strategy, an Intel spokesperson told CRN.

All RealSense products will be discontinued, though it appears its stereo cameras for depth perception will stay, to some degree, according to IEEE's Spectrum.

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A man spent a year in jail on a murder charge that hinged on disputed AI evidence. Now the case has been dropped - The Register

Imagine youre having friends over for lunch and plan to order a pepperoni pizza. You recall Amy mentioning that Susie had stopped eating meat. You try calling Susie, but when she doesnt pick up, you decide to play it safe and just order a margherita pizza instead.

People take for granted the ability to deal with situations like these on a regular basis. In reality, in accomplishing these feats, humans are relying on not one but a powerful set of universal abilities known as common sense.

As an artificial intelligence researcher, my work is part of a broad effort to give computers a semblance of common sense. Its an extremely challenging effort.

Despite being both universal and essential to how humans understand the world around them and learn, common sense has defied a single precise definition. G. K. Chesterton, an English philosopher and theologian, famously wrote at the turn of the 20th century that common sense is a wild thing, savage, and beyond rules. Modern definitions today agree that, at minimum, it is a natural, rather than formally taught, human ability that allows people to navigate daily life.

Common sense is unusually broad and includes not only social abilities, like managing expectations and reasoning about other peoples emotions, but also a naive sense of physics, such as knowing that a heavy rock cannot be safely placed on a flimsy plastic table. Naive, because people know such things despite not consciously working through physics equations.

Common sense also includes background knowledge of abstract notions, such as time, space and events. This knowledge allows people to plan, estimate and organize without having to be too exact.

Intriguingly, common sense has been an important challenge at the frontier of AI since the earliest days of the field in the 1950s. Despite enormous advances in AI, especially in game-playing and computer vision, machine common sense with the richness of human common sense remains a distant possibility. This may be why AI efforts designed for complex, real-world problems with many intertwining parts, such as diagnosing and recommending treatments for COVID-19 patients, sometimes fall flat.

Modern AI is designed to tackle highly specific problems, in contrast to common sense, which is vague and cant be defined by a set of rules. Even the latest models make absurd errors at times, suggesting that something fundamental is missing in the AIs world model. For example, given the following text:

You poured yourself a glass of cranberry, but then absentmindedly, you poured about a teaspoon of grape juice into it. It looks OK. You try sniffing it, but you have a bad cold, so you cant smell anything. You are very thirsty. So you

the highly touted AI text generator GPT-3 supplied

drink it. You are now dead.

Recent ambitious efforts have recognized machine common sense as a moonshot AI problem of our times, one requiring concerted collaborations across institutions over many years. A notable example is the four-year Machine Common Sense program launched in 2019 by the U.S. Defense Advanced Research Projects Agency to accelerate research in the field after the agency released a paper outlining the problem and the state of research in the field.

The Machine Common Sense program funds many current research efforts in machine common sense, including our own, Multi-modal Open World Grounded Learning and Inference (MOWGLI). MOWGLI is a collaboration between our research group at the University of Southern California and AI researchers from the Massachusetts Institute of Technology, University of California at Irvine, Stanford University and Rensselaer Polytechnic Institute. The project aims to build a computer system that can answer a wide range of commonsense questions.

One reason to be optimistic about finally cracking machine common sense is the recent development of a type of advanced deep learning AI called transformers. Transformers are able to model natural language in a powerful way and, with some adjustments, are able to answer simple commonsense questions. Commonsense question answering is an essential first step for building chatbots that can converse in a human-like way.

In the last couple of years, a prolific body of research has been published on transformers, with direct applications to commonsense reasoning. This rapid progress as a community has forced researchers in the field to face two related questions at the edge of science and philosophy: Just what is common sense? And how can we be sure an AI has common sense or not?

To answer the first question, researchers divide common sense into different categories, including commonsense sociology, psychology and background knowledge. The authors of a recent book argue that researchers can go much further by dividing these categories into 48 fine-grained areas, such as planning, threat detection and emotions.

However, it is not always clear how cleanly these areas can be separated. In our recent paper, experiments suggested that a clear answer to the first question can be problematic. Even expert human annotators people who analyze text and categorize its components within our group disagreed on which aspects of common sense applied to a specific sentence. The annotators agreed on relatively concrete categories like time and space but disagreed on more abstract concepts.

Even if you accept that some overlap and ambiguity in theories of common sense is inevitable, can researchers ever really be sure that an AI has common sense? We often ask machines questions to evaluate their common sense, but humans navigate daily life in far more interesting ways. People employ a range of skills, honed by evolution, including the ability to recognize basic cause and effect, creative problem solving, estimations, planning and essential social skills, such as conversation and negotiation. As long and incomplete as this list might be, an AI should achieve no less before its creators can declare victory in machine commonsense research.

Its already becoming painfully clear that even research in transformers is yielding diminishing returns. Transformers are getting larger and more power hungry. A recent transformer developed by Chinese search engine giant Baidu has several billion parameters. It takes an enormous amount of data to effectively train. Yet, it has so far proved unable to grasp the nuances of human common sense.

Even deep learning pioneers seem to think that new fundamental research may be needed before todays neural networks are able to make such a leap. Depending on how successful this new line of research is, theres no telling whether machine common sense is five years away, or 50.

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An AI expert explains why it's hard to give computers something you take for granted: Common sense - The Conversation US

A Honda autonomous vehicle bot with a compatible AI assistant, conceptualized for the year 2040 where companionship with robotic machines is going to be a common affair.

Just imagine how overurbanization by the year 2040 will change the complexion of living. Due to increased expenses and the dreams of Generation Z, the number of single households will increase exponentially. Solitary life will be more common and interaction with Artificial Intelligence will be the solution to the widespread loneliness. Jack Junseok Lees 2040 Honda NIKO bot is that very friend, as in Toy Story words, youve got a friend in him!

This smart companion has a frontal face with larger proportions to emphasize the living character, making the interaction very lively. The animated design of the fenders with covered wheels looks like the legs of a pet animal. This creates an illusion of a seemingly moving gesture like that of a living being. Most of all NIKO has a proud stance like the Lion King while radiating that cute character of a playful puppy. According to Jack, the design philosophy of the bot is centered around creating a very lively object.

The bot inside the movable vehicle will understand the owners emotion and current state of mind to provide empathy to them. Itll laugh or cry with them, hear their problems and give unique solutions or thoughts. It also has storage on both sides to haul any groceries or other things if your own hands are full. When in an open position, these doors act as side tables to keep things.

This is combined with an autonomous vehicle-like bot which is bigger in size which serves as a compact commuter for short city stints to get essentials from the nearby store. Both these AI machines in a way provide the user with genuine support just like a human would do.

Designer: Jack Junseok Lee

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Honda 2040 NIKO comes with a tiny Ai assistant, taking the car from a vehicle to your friend! - Yanko Design

A multidisciplinary group of Stanford University professors and students wants to start a serious discussion about the increasing use of large, frighteningly smart, foundation AI models such as OpenAIs GPT-3 (Generative Pretraining Transformer 3) natural language model.

GPT-3 is foundational because it was developed using huge quantities of training data and computer power to reach state-of-the-art, general-purpose performance. Developers, not wanting to reinvent the wheel, are using it as the basis for their software to tackle specific tasks.

But foundation models have some very real downsides, explains Stanford computer science professor Percy Liang. They create a single point of failure, so any defects, any biases which these models have, any security vulnerabilities . . . are just blindly inherited by all the downstream tasks, he says.

Liang leads a new group assembled by Stanfords institute for Human-Centered Artificial Intelligence (HAI) called the Center for Research on Foundation Models (CRFM). The group is studying the impacts and implications of foundation models, and its inviting the tech companies developing them to come to the table and participate.

The profit motive encourages companies to punch the gas on emerging tech instead of braking for reflection and study, says Fei-Fei Li, who was the director of Stanfords AI Lab from 2013 to 2018 and now codirects HAI.

Industry is working fast and hard on this, but we cannot let them be the only people who are working on this model, for multiple reasons, Li says. A lot of innovation that could come out of these models still, I firmly believe will come out of the research environment where revenue is not the goal.

Part of the reason for all the concern is that foundation models end up touching the experience of so many people. In 2019, researchers at Google built the transformational BERT (Bidirectional Encoder Representations from Transformers) natural language model, which now plays a role in nearly all of Googles search functions. Other companies took BERT and built new models on top of it. Researchers at Facebook, for example, used BERT as the basis for an even larger natural language model, called RoBERTa (Robustly Optimized BERT Pretraining Approach), which now underpins many of Facebooks content moderation models.

Now almost all NLP (Natural Language Processing) models are built on top of BERT, or maybe one of a few of these foundation models, Liang says. So theres this incredible homogenization thats happening.

In June 2020 OpenAI began making its GPT-3 natural language model available via a commercial API to other companies that then built specialized applications on top of it. OpenAI has now built a new model, Codex, that creates computer code from English text.

With all due respect to industry, they cannot have the law school and medical school on their campus.

You train a huge model and then you go in and you discover what it can do, discover what has emerged from the process, says Liang. Thats a fascinating thing for scientists to study, he adds, but sending the models into production when theyre not fully understood is dangerous.

We dont even know what theyre capable of doing, let alone when they fail, he says. Now things get really interesting, because were building our entire AI infrastructure on these models.

If biases are baked into models such as GPT-3 and BERT, they may infect applications built on top of them. For example, a recent study by Stanford HAI researchers involved teaching GPT-3 to compose stories beginning with the phrase two Muslims walk into a . . .. Sixty-six percent of the text the model provided involved violent themes, a far higher percentage than for other groups. Other researchers have uncovered other instances of deep-rooted biases in foundation models: In 2019, for instance, BERT was shown to associate terms such as programmer with men over women.

To be sure, companies employ ethics teams and carefully select training data that will not introduce biases into their models. And some take steps to prevent their foundation models from providing the basis for unethical applications. OpenAI, for example, pledges to cut off API access to any application used for harassment, spam, radicalization, or astroturfing.

Still, private companies wont necessarily comply with a set of industry standards for ensuring unbiased models. And there is no regulatory body at the state or federal level thats ready with policies that might keep large AI models from impacting consumers, especially those in minority or underrepresented groups, in negative ways. Li says lawmakers have attended past HAI workshops, hoping to gain insights on what policies might look like.

She also stresses that its the university setting that can provide all the necessary perspectives for defining policies and standards.

We not only have deep experts from philosophy, political science, and history departments, we also have a medical school, business school, and law school, and we also have experts in application areas that come to work on these critical technologies with us, Li says. And with all due respect to industry, they cannot have the law school and medical school on their campus. (Li worked at Google as chief scientist for AI and machine learning 20172018.)

One of the first products of CRFMs work is a 200-page research paper on foundation models. The paper, which is being published today, was cowritten by more than 100 authors of different professional disciplines. It explores 26 aspects of foundation models, including the legal ramifications, environmental and economic impacts, and ethical issues.

CRFM will also hold a (virtual) workshop later this month at which its members will discuss foundation models with visiting academics and people from the tech industry.

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Stanford AI experts warn of biases in GPT-3 and BERT models - Fast Company

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When pharmaceutical company Moderna announced the first clinical trial of a COVID-19 vaccine, it was a proud moment but not a surprising one for Dave Johnson, the companys chief data and artificial intelligence officer.

When Johnson joined the company in 2014, he helped put in place automated processes and AI algorithmsto increase the number of small-scale messenger RNA (mRNA) needed to run clinical experiments. This groundwork contributed to Moderna releasing one of the first COVID-19 vaccines (using mRNA) even as the world had only started to understand the virus threat.

The whole COVID vaccine development, were immensely proud of the work that weve done there, and were immensely proud of the superhuman effort that our people went through to bring it to market so quickly, Johnson said during a bonus episode of the MIT Sloan Management Review podcast Me, Myself, and AI.

But a lot of it was built on this infrastructure that we had put in place where we didnt build algorithms specifically for COVID; we just put them through the same pipeline of activity that weve been doing, Johnson said. We just turned it as fast as we could.

Successfully using AI in business is at the heart of the podcast, which recently finished its second season. The podcast is hosted by Sam Ransbotham, professor of information systems at Boston College, and Shervin Khodabandeh, senior partner with Boston Consulting Group and co-lead of its AI practice in North America. The series features leaders who are achieving big wins with AI.

Heres a look at some of the highlights from this season.

If youre frantically searching the Home Depot website for a way to patch a hole in your wall, chances are youre not thinking of the people whove generated the recommendation for the correct brackets to use with your new wall-mounted mirror or the project guide for the repairs youre doing.

But Huiming Qu, the Home Depots senior director of data science and machine learning products, marketing, and online, is not only thinking about those data scientists and engineers, shes leading them, and doing it in a way she hopes will leave both her team and customers happy. To do this, Qus team pulls as much data as it can from customer visits to the site, such as what was in their carts and what their prior searches were.

Qus team then weaves that information into an extremely, extremely light test version of an algorithm to cut down on development time and to figure out if that change will be possible within Home Depots digital infrastructure.

It takes a cross-functional team iteratively to move a lot faster to break down that bigger problem, bigger goals, to many smaller ones that we can achieve very quickly, Qu said.

When it comes to AI and machine learning at Google, the tech company applies three principles to innovation: focus on the user, rapidly prototype, and think in 10x.

We want to make sure were solving for a problem that also has the scale that will be worth it and really advances whatever were trying to do not in a small way, but in a really big way, said Will Grannis, managing director of Google Clouds Office of the CTO.

But before Google puts too many resources behind these 10x or moonshot solutions, engineers are encouraged to take on roof shot projects.

Rather than aiming for the sky right out of the gate, engineers only have to get an idea to the roof, Grannis said. A moonshot is often the product of a series of smaller roof shots, he said, and this approach allows him to see who is willing to put in the effort to see something through from start to finish.

If people dont believe in the end state, the big transformation, theyre usually much less likely to journey across those roof shots and to keep going when things get hard, Grannis said. My job is to create an environment where people feel empowered, encouraged, and excited to try and [I] try to demotivate them as little as possible, because theyll find their way to the roof shot, and then the next one, and then the next one, and then pretty soon youre three years in, and I couldnt stop a project if I wanted to.

JoAnn Stonier, chief data officer at Mastercard is using AI and machine learning to prevent and uncover bias, even though most datasets will have some bias in them to begin with.

And thats OK. The 1910 U.S. voter rolls, for example, are a dataset, Stonier said. They could be used to study something like voting habits of early 20th century white men. But you would also need to acknowledge that women and people of color are missing from that dataset, so your study wouldnt reflect the entire U.S. population in 1910.

The problem is, if you dont remember that, or youre not mindful of that, then you have an inquiry thats going to learn off of a dataset that is missing characteristics that [are] going to be important to whatever that other inquiry is, Stonier said. Those are some of the ways that I think we can actually begin to design a better future, but it means really being very mindful of whats inherent in the dataset, whats there, whats missing but also can be imputed.

The complete two seasons of Me, Myself, and AI can be listened to on Apple Podcasts and Spotify.Transcripts of the Me, Myself, and AI podcast are also available.

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How Moderna, Home Depot, and others are succeeding with AI - MIT Sloan News

DUBLIN--(BUSINESS WIRE)--The "In-Cabin Automotive AI Market - A Global and Regional Analysis: Focus on Product Types, Applications, and Country Assessment - Analysis and Forecast, 2020-2026" report has been added to ResearchAndMarkets.com's offering.

The global in-cabin automotive AI market is expected to reach $503.2 million by 2026, with a CAGR of 45.41% during the forecast period of 2021-2026.

One of the most successful drivers in promoting customer loyalty is in-cabin AI and the experience it enables. The automakers are engaging with or looking to engage with key ecosystem providers to give additional value to their customers. Improved driver experience and safety, as well as intelligent in-car assistance, are among some of the advantages offered by AI-powered cabins.

The in-cabin experience is usually referred to as the AI-powered cockpit, but it's beyond the driver's perspective. In-car experiences encompass the entire user experience, including the driver and passengers, with the goal of improving the overall comfort of the in-cabin experience.

This includes the application of AI in intelligent driver assistance programs that improve safety or in infotainment systems that give instructions to the driver while providing content recommendations to passengers in the seat.

Market Segmentation In-Cabin Automotive AI Market by Product

The global in-cabin automotive AI market has been segmented based on product including camera, radar, voice assistant, smart sensor. Among the various types of products, the camera holds the highest share as the monitoring systems contain a charge-coupled device (CCD) camera that can track the driver's eye and is mounted on the steering column. In case of an emergency situation, the system alerts the driver with warning sounds and flashing lights.

If the driver fails to react in a timely manner, the vehicle will automatically apply the brakes. A driver monitoring system (DMS) uses an infrared light-emitting diode (LED) and a camera to monitor the driver for signs of inattention or tiredness.

In-Cabin Automotive AI Market by Application

The global in-cabin automotive AI market has been segmented based on application driver monitoring system, occupant monitoring system, conversation assistance, smart HVAC. The driver monitoring system integrates the use of sensors, cameras, and other 'intelligent' equipment to provide aid, direction, and warning to the driver in a variety of crucial situations and emergencies. Moreover, the rising number of traffic accidents globally as a result of less driver alertness has become a serious area of concern for the governments.

In-Cabin Automotive AI Market by Region

The regions detailed in this report include North America, Europe, the U.K., China, Asia-Pacific and Japan, South America, and the Middle East and Africa. Europe holds the largest market share in the global in-cabin automotive AI market.

The economic situation in Europe is favorable, and the consumers have a high disposable income, thereby leading to high demand for technologies such as in-cabin automotive AI.

Image sensing/cameras, infrared sensing, and strain gauges are among the region's signature technologies related to in-cabin automotive AI. Along with this, manufacturers are looking to improve their flexibility across the value chain, thereby driving the market toward the adoption of in-cabin automotive AI.

The companies profiled in the report have been selected post undergoing in-depth interviews with experts and understanding details around companies such as product portfolios, annual revenues, market penetration, research and development initiatives, and domestic and international presence in the in-cabin automotive AI market.

Key Market Players and Competition Synopsis

Some of the key players operating in the market include

Key Questions Answered in the Report

Key Topics Covered:

1 Markets

1.1 Industry Outlook

1.1.1 Trends: Industry Dynamics Defining Future Trends In-Cabin Automotive AI Market

1.1.1.1 Next-Generation In-Cabin Automotive AI

1.1.1.2 Growing Trend for Digital Cockpit

1.1.2 Market Drivers

1.1.2.1 Rising Demand for the Autonomous Vehicle

1.1.2.2 Increasing Concerns of Passenger and Pedestrian Safety Increase the Demand for In-Cabin Automotive AI

1.1.2.3 OEMs and government regulatory authorities are taking necessary steps to improve transportation by addressing major challenges like road accidents and traffic congestion

1.1.2.4 Rising Demand for Customized Consumer Experience

1.1.2.5 Impact of Business Drivers

1.1.3 Market Restraints

1.1.3.1 Challenges Related to Infrastructure

1.1.3.2 Design Challenges

1.1.3.3 Impact of Business Restraints

1.1.4 Market Opportunities

1.1.4.1 Increasing Demand for HVAC Systems in Cockpit

1.1.4.2 Increasing Demand for Occupant Safety and Security

1.1.4.3 Impact of Business Opportunities

1.1.5 Key Developments and Strategies

1.1.5.1 Product Developments

1.1.5.2 Market Developments

1.1.5.3 Mergers & Acquisitions

1.1.5.4 Partnerships & Joint Ventures

1.2 Supply Chain Analysis

2 Application

2.1 Global In-Cabin Automotive AI Market Application and Specification

2.1.1 Occupant Monitoring System

2.1.2 Driver Monitoring System

2.1.3 Conversation Assistance

2.1.4 Smart HVAC

2.2 Demand Analysis of In-Cabin Automotive AI Market (by Application)

2.2.1 Occupant Monitoring System

2.2.2 Driver Monitoring System

2.2.3 Conversation Assistance

2.2.4 Smart HVAC

3 Products

3.1 Global In-Cabin Automotive AI Market (by Product)

3.1.1 Radar

3.1.2 Camera

3.1.3 Voice Assistant

3.1.4 Smart Sensor

3.2 Demand Analysis of In-Cabin Automotive AI Market (by Product)

3.2.1 Radar

3.2.2 Camera

3.2.3 Voice Assistant

3.2.4 Smart Sensor

4 Regional Analysis

4.1 Market

4.1.1 Buyers Attribute

4.1.2 Key Players

4.1.3 Competitive Benchmarking

4.1.4 Business Challenges

4.1.5 Business Drivers

4.2 Applications

5 Markets - Competitive Benchmarking & Company Profiles

5.1 Competitive Benchmarking

5.2 Company Profiles

5.2.1 Company Overview

5.2.2 Business Strategies

5.2.3 Corporate Strategies

5.2.4 Competitive Position

6 Research Methodology

For more information about this report visit https://www.researchandmarkets.com/r/q9hk0q

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Global In-Cabin Automotive AI (Artificial Intelligence) Market Report 2021: Opportunities with Increasing Demand for HVAC Systems & Occupant...

For entrepreneurial MIT students looking to put their skills to work for a greater good, the Media Arts and Sciences class MAS.664 (AI for Impact) has been a destination point. With the onset of the pandemic, that goal came into even sharper focus. Just weeks before the campus shut down in 2020, a team of students from the class launched a project that would make significant strides toward an open-source platform to identify coronavirus exposures without compromising personal privacy.

Their work was at the heart of Safe Paths, one of the earliest contact tracing apps in the United States. The students joined with volunteers from other universities, medical centers, and companies to publish their code, alongside a well-received white paper describing the privacy-preserving, decentralized protocol, all while working with organizations wishing to launch the app within their communities. The app and related software eventually got spun out into the nonprofit PathCheck Foundation, which today engages with public health entities and is providing exposure notifications in Guam, Cyprus, Hawaii, Minnesota, Alabama, and Louisiana.

The formation of Safe Paths demonstrates the special sense among MIT researchers that we can launch something that can help people around the world, notes Media Lab Associate Professor Ramesh Raskar, who teaches the class together with Media Lab Professor Alex Sandy Pentland and Media Lab Lecturer Joost Bonsen. To have that kind of passion and ambition but also the confidence that what you create here can actually be deployed globally is kind of amazing.

AI for Impact, created by Pentland, began meeting two decades ago under the course name Development Ventures, and has nurtured multiple thriving businesses. Examples of class ventures that Pentland incubated or co-founded include Dimagi, Cogito, Ginger, Prosperia, and Sanergy.

The aim-high challenge posed to each class is to come up with a business plan that touches a billion people, and it cant all be in one country, Pentland explains. Not every class effort becomes a business, but 20 percent to 30 percent of students start something, which is great for an entrepreneur class, says Pentland.

Opportunities for Impact

The numbers behind Dimagi, for instance, are striking. Its core product CommCare has helped front-line health workers provide care for more than 400 million people in more than 130 countries around the world. When it comes to maternal and child care, Dimagi's platform has registered one in every 110 pregnancies worldwide. This past year, several governments around the world deployed CommCare applications for Covid-19 response from Sierra Leone and Somalia to New York and Colorado.

Spinoffs like Cogito, Prosperia, and Ginger have likewise grown into highly successful companies. Cogito helps a million people a day gain access to the health care they need; Prosperia helps manage social support payments to 80 million people in Latin America; and Ginger handles mental health services for over 1 million people.

The passion behind these and other class ventures points to a central idea of the class, Pentland notes: MIT students are often looking for ways to build entrepreneurial businesses that enable positive social change.

During the spring 2021 class, for example, a number of promising student projects included tools to help residents of poor communities transition to owning their homes rather than renting, and to take better control of their community health.

Its clear that the people who are graduating from here want to do something significant with their lives ... they want to have an impact on their world, Pentland says. "This class enables them to meet other people who are interested in doing the same thing, and offers them some help in starting a company to do it.

Many of the students who join the class come in with a broad set of interests. Guest lectures, case studies of other social entrepreneurship projects, and an introduction to a broad ecosystem of expertise and funding, then helps students to refine their general ideas into specific and viable projects.

A path toward confronting a pandemic

Raskar began co-teaching the class in 2019, and brought a Big AI focus to the Development Ventures class, inspired by an AI for Impact team he had set up at his former employer, Facebook. What I realized is that companies like Google or Facebook or Amazon actually have enough data about all of us that they can solve major problems in our society climate, transportation, health, and so on, he says. This is something we should think about more seriously: how to use AI and data for positive social impact, while protecting privacy.

Early into the spring 2020 class, as students were beginning to consider their own projects, Raskar approached the class about the emerging coronavirus outbreak. Students like Kristen Vilcans recognized the urgency, and the opportunity. She and 10 other students joined forces to work on a project that would focus on Covid-19.

"Students felt empowered to do something to help tackle the spread of this alarming new virus," Raskar recalls. "They immediately began to develop data- and AI-based solutions to one of the most critical pieces of addressing a pandemic: halting the chain of infections. They created and launched one of the first digital contact tracing and exposure notification solutions in the U.S., developing an early alert system that engaged the public and protected privacy.

Raskar looks back on the moment when a core group of students coalesced into a team. It was very rare for a significant part of the class to just come together saying, 'lets do this, right away.' It became as much a movement as a venture.

Group discussions soon began to center around an open-source, privacy-first digital set of tools for Covid-19 contact tracing. For the next two weeks, right up to the campus shutdown in March 2020, the team took over two adjacent conference rooms in the Media Lab, and started a Slack messaging channel devoted to the project. As the team members reached out to an ever-wider circle of friends, colleagues, and mentors, the number of participants grew to nearly 1,600 people, coming together virtually from all corners of the world.

Kaushal Jain, a Harvard Business School student who had cross-registered for the spring 2020 class to get to know the MIT ecosystem, was also an early participant in Safe Paths. He wrote up an initial plan for the venture and began working with external organizations to figure out how to structure it into a nonprofit company. Jain eventually became the project's lead for funding and partnerships.

Vilcans, a graduate student in system design and management, served as Safe Paths communications lead through July 2020, while still working a part-time job at Draper Laboratory and taking classes.

There are these moments when you want to dive in, you want to contribute and you want to work nonstop, she says, adding that the experience was also a wake-up call on how to manage burnout, and how to balance what you need as a person while contributing to a high-impact team. That's important to understand as a leader for the future.

MIT recognized Vilcan's contributions later that year with the 2020 SDM Student Award for Leadership, Innovation, and Systems Thinking.

Jain, too, says the class gave him more than he could have expected.

I made strong friendships with like-minded people from very different backgrounds, he says. One key thing that I learned was to be flexible about the kind of work you want to do. Be open and see if there's an opportunity, either through crisis or through something that you believe could really change a lot of things in the world. And then just go for it.

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AI for Impact lives up to its name - MIT News

After a two-year battle with throat cancer and a tracheotomy that severely affected his speech, Val Kilmer has reclaimed his voice through AI technology.

UK software company Sonantic used old recordings of the Top Gun actors voice to recreate a computer-generated version.

The company, known for its voice cloning work, shared a clip of the results on its official YouTube channel earlier in August.

"My voice as I knew it was taken away from me. People around me struggle to understand when I'm talking, Kilmer, 61, is heard saying in the clip through his AI voice. But despite all that I still feel, I'm the exact same person. Still the same creative soul. A soul that dreams ideas and stories confidently, but now I can express myself again, bring these ideas to you, and show you this part of myself once more. A part that was never truly gone. Just hiding away.

Kilmer had an active part in developing the AI voice, online news website The Wrap reports. The Batman Forever star provided the archival footage of his voice, which was then used to create the prototype.

Kilmer's AI voice is not featured in the documentary 'Val' but moving forward, the actor will be able to use his new voice in both a professional and personal capacity. Amazon Prime Video via AP

"I'm grateful to the entire team at Sonantic who masterfully restored my voice in a way I've never imagined possible, Kilmer told The Wrap. "As human beings, the ability to communicate is the core of our existence and the side effects from throat cancer have made it difficult for others to understand me. The chance to narrate my story, in a voice that feels authentic and familiar, is an incredibly special gift.

Kilmers throat cancer diagnosis was not publicly confirmed until 2017, two years after the actor was first hospitalised for the condition. By then, he had undergone chemotherapy and a tracheotomy procedure that abraded his voice to a rasp. In 2020, the actor revealed that he had been cancer-free for four years.

News of Kilmers new computer-generated voice comes just after the release of his documentary Val on Amazon Prime Video. Co-produced by the actors daughter and son, the film examines Kilmers life and career, as well as his cancer recovery.

The AI voice is not featured in Val, but moving forward, Kilmer will be able to use his new voice in both a professional and personal capacity.

Updated: August 21st 2021, 12:32 PM

Results

Stage 7:

1.Caleb Ewan (AUS) Lotto Soudal -3:18:29

2.Sam Bennett (IRL) Deceuninck-QuickStep - sametime

3.Phil Bauhaus (GER) Bahrain Victorious

4.Michael Morkov (DEN) Deceuninck-QuickStep

5.Cees Bol (NED) Team DSM

General Classification:

1.Tadej Pogacar (SLO) UAE Team Emirates - 24:00:28

2.Adam Yates (GBR) Ineos Grenadiers -0:00:35

3.Joao Almeida (POR) Deceuninck-QuickStep -0:01:02

4.Chris Harper (AUS) Jumbo-Visma -0:01:42

5.Neilson Powless (USA) EF Education-Nippo -0:01:45

Results

Stage 7:

1.Caleb Ewan (AUS) Lotto Soudal -3:18:29

2.Sam Bennett (IRL) Deceuninck-QuickStep - sametime

3.Phil Bauhaus (GER) Bahrain Victorious

4.Michael Morkov (DEN) Deceuninck-QuickStep

5.Cees Bol (NED) Team DSM

General Classification:

1.Tadej Pogacar (SLO) UAE Team Emirates - 24:00:28

2.Adam Yates (GBR) Ineos Grenadiers -0:00:35

3.Joao Almeida (POR) Deceuninck-QuickStep -0:01:02

4.Chris Harper (AUS) Jumbo-Visma -0:01:42

5.Neilson Powless (USA) EF Education-Nippo -0:01:45

Results

Stage 7:

1.Caleb Ewan (AUS) Lotto Soudal -3:18:29

2.Sam Bennett (IRL) Deceuninck-QuickStep - sametime

3.Phil Bauhaus (GER) Bahrain Victorious

4.Michael Morkov (DEN) Deceuninck-QuickStep

5.Cees Bol (NED) Team DSM

General Classification:

1.Tadej Pogacar (SLO) UAE Team Emirates - 24:00:28

2.Adam Yates (GBR) Ineos Grenadiers -0:00:35

3.Joao Almeida (POR) Deceuninck-QuickStep -0:01:02

4.Chris Harper (AUS) Jumbo-Visma -0:01:42

5.Neilson Powless (USA) EF Education-Nippo -0:01:45

Results

Stage 7:

1.Caleb Ewan (AUS) Lotto Soudal -3:18:29

2.Sam Bennett (IRL) Deceuninck-QuickStep - sametime

3.Phil Bauhaus (GER) Bahrain Victorious

4.Michael Morkov (DEN) Deceuninck-QuickStep

5.Cees Bol (NED) Team DSM

General Classification:

1.Tadej Pogacar (SLO) UAE Team Emirates - 24:00:28

2.Adam Yates (GBR) Ineos Grenadiers -0:00:35

3.Joao Almeida (POR) Deceuninck-QuickStep -0:01:02

4.Chris Harper (AUS) Jumbo-Visma -0:01:42

5.Neilson Powless (USA) EF Education-Nippo -0:01:45

Results

Stage 7:

1.Caleb Ewan (AUS) Lotto Soudal -3:18:29

2.Sam Bennett (IRL) Deceuninck-QuickStep - sametime

3.Phil Bauhaus (GER) Bahrain Victorious

4.Michael Morkov (DEN) Deceuninck-QuickStep

5.Cees Bol (NED) Team DSM

General Classification:

1.Tadej Pogacar (SLO) UAE Team Emirates - 24:00:28

2.Adam Yates (GBR) Ineos Grenadiers -0:00:35

3.Joao Almeida (POR) Deceuninck-QuickStep -0:01:02

4.Chris Harper (AUS) Jumbo-Visma -0:01:42

5.Neilson Powless (USA) EF Education-Nippo -0:01:45

Results

Stage 7:

1.Caleb Ewan (AUS) Lotto Soudal -3:18:29

2.Sam Bennett (IRL) Deceuninck-QuickStep - sametime

3.Phil Bauhaus (GER) Bahrain Victorious

4.Michael Morkov (DEN) Deceuninck-QuickStep

5.Cees Bol (NED) Team DSM

General Classification:

1.Tadej Pogacar (SLO) UAE Team Emirates - 24:00:28

2.Adam Yates (GBR) Ineos Grenadiers -0:00:35

3.Joao Almeida (POR) Deceuninck-QuickStep -0:01:02

4.Chris Harper (AUS) Jumbo-Visma -0:01:42

5.Neilson Powless (USA) EF Education-Nippo -0:01:45

Results

Stage 7:

1.Caleb Ewan (AUS) Lotto Soudal -3:18:29

2.Sam Bennett (IRL) Deceuninck-QuickStep - sametime

3.Phil Bauhaus (GER) Bahrain Victorious

4.Michael Morkov (DEN) Deceuninck-QuickStep

5.Cees Bol (NED) Team DSM

General Classification:

1.Tadej Pogacar (SLO) UAE Team Emirates - 24:00:28

2.Adam Yates (GBR) Ineos Grenadiers -0:00:35

3.Joao Almeida (POR) Deceuninck-QuickStep -0:01:02

Go here to see the original:

Val Kilmer reclaims his voice through AI technology after throat cancer - The National