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In the week that drew 2021 to a close, the tech news cycle died down, as it typically does. Even an industry as fast-paced as AI needs a reprieve, sometimes especially as a new COVID-19 variant upends plans and major conferences.

But that isnt to say late December wasnt eventful.

One of the most talked-about stories came from the South China Morning Post (SCMP), which described an AI prosecutor developed by Chinese researchers that can reportedly identify crimes and press charges with 97% accuracy. The system which was trained on 1,000 traits sourced from 17,000 real-life cases of crimes from 2015 to 2020, like gambling, reckless driving, theft, and fraud recommends sentences given a brief text description. Its already been piloted in the Shanghai Pudong Peoples Procuratorate, Chinas largest district prosecution office., according to SCMP.

It isnt surprising that a country like China which, like parts of the U.S., has embraced predictive crime technologies is pursuing a black-box stand-in for human judges. But the implications are nonetheless worrisome for those who might be subjected to the AI prosecutors judgment, given how inequitable algorithms in the justice system have historically been shown to be.

Published last December, a study from researchers at Harvard and the University of Massachusetts found that the Public Safety Assessment (PSA), a risk-gauging tool that judges can opt to use when deciding whether a defendant should be released before a trial, tends to recommend sentencing thats too severe. Moreover, the PSA is likely to impose a cash bond on male arrestees versus female arrestees, according to the researchers a potential sign of gender bias.

The U.S. justice system has a history of adopting AI tools that are later found to exhibit bias against defendants belonging to certain demographic groups. Perhaps the most infamous of these is Northpointes Correctional Offender Management Profiling for Alternative Sanctions (COMPAS), which is designed to predict a persons likelihood of becoming a recidivist. A ProPublica report found that COMPAS was far more likely to incorrectly judge black defendants to be at higher risk of recidivism than white defendants, while at the same time flagging white defendants as low risk more often than black defendants.

With new research showing that even training predictive policing tools in a way meant to lessen bias has little effect, its become clear if it wasnt before that deploying these systems responsibly today is infeasible. Thats perhaps why some early adopters of predictive policing tools, like the police departments of Pittsburgh and Los Angeles, have announced they will no longer use them.

But with less scrupulous law enforcement, courtrooms, and municipalities plowing ahead, regulation-driven by public pressure is perhaps the best bet for reigning in and setting standards for the technology. Cities including Santa Cruz and Oakland have outright banned predictive policing tools, as has New Orleans. And the nonprofit group Fair Trials is calling on the European Union to include a prohibition on predictive crime tools in its proposed AI regulatory framework.

We do not condone the use [of tools like the PSA], Ben Winters, the creator of a report from the Electronic Privacy Information Center that called pretrial risk assessment tools a strike against individual liberties, said in a recent statement. But we would absolutely say that where they are being used, they should be regulated pretty heavily.

Its unclear whether even the most sophisticated AI systems understand the world the way that humans do. Thats another argument in favor of regulating predictive policing, but one company, Cycorp which was profiled by Business Insider this week is seeking to codify general human knowledge so that AI might make use of it.

Cycorps prototype software, which has been in development for nearly 30 years, isnt programmed in the traditional sense. Cycorp can make inferences that an author might expect a human reader to make. Or it can pretend to be a confused sixth-grader, tasking users with helping it to learn sixth-grade math.

Is there a path to AI with human-level intelligence? Thats the million-dollar question. Experts like the vice president and chief AI scientist for Facebook, Yann LeCun, and renowned professor of computer science, and artificial neural networks expert, Yoshua Bengio, dont believe its within reach, but others beg to differ. One promising direction is neuro-symbolic reasoning, which merges learning and logic to make algorithms smarter. The thought is that neuro-symbolic reasoning could help incorporate common sense reasoning and domain knowledge into algorithms to, for example, identify objects in a picture.

New paradigms could be on the horizon, like synthetic brains made from living cells. Earlier this month, researchers at Cortical Labs created a network of neurons in a dish that learned to play Pong faster than an AI system. The neurons werent as skilled at Pong as the system, but they took only five minutes to master the mechanics versus the AIs 90 minutes.

Pong hardly mirrors the complexity of the real world. But in tandem with forward-looking hardware like neuromorphic chips and photonics, as well as novel scaling techniques and architectures, the future looks bright for more capable, potentially human-like AI. Regulation will catch up, with any luck. Weve seen a preview of the consequences including wrongful arrests, sexist job recruitment, anderroneous grades if it doesnt.

For AI coverage, send news tips toKyle Wiggers and be sure to subscribe to the AI Weekly newsletterand bookmark our AI channel,The Machine.

Thanks for reading,

Kyle Wiggers

AI Staff Writer

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AI Weekly: AI prosecutors and pong-playing neurons closed out 2021 - VentureBeat

Christmas 2021 might seem like a lot of deja vu, amid reports of COVID cases skyrocketing and local businesses closing because of outbreaks. But the pandemic is in a very different place than it was last year. There are vaccines, boosters, and antiviral treatments coming soon. And unlike last year, the CDC isn't advising Americans against all holiday travel. This year, experts are encouraging vaccinated Americans to celebrate with loved oneswith some important caveats. Some places are essentially swarming with COVID and should be avoided or only visited after taking precautions to avoid infection. Read on to find out moreand to ensure your health and the health of others, don't miss these Sure Signs You've Already Had COVID.

Dr. Anthony Fauci, the nation's top infectious-disease expert, has been urging Americans to enjoy the holidays with family, as long as everyone is vaccinated. "But I want to make sure this is not confused with going to a large gathering and there are many of these parties that have 30, 40, 50 people in which you do not know the vaccination status of individuals," he said on Thursday. "Those are the kind of functions in the context of COVID and particularly in the context of Omicron that you do not want to go to."

"Given the number of new cases reported daily, infected people are at airports and getting onto airplanes," said Sheldon H. Jacobson, a professor of computer science at the University of Illinois at Urbana-Champaign who studies public health and aviation security, told NBC News this week. "The riskiest part of air travel is the time before and after flights, not during flights. Waiting in a terminal prior to boarding is a vulnerable time and environment for virus spread." Your move: Be vigilant about wearing a high-quality mask (like an N95, KN95 or surgical mask) at all times. If you haven't upgraded from a cloth mask, now is the time.

RELATED: Omicron Symptoms Most Commonly Appear Like This

Crowded Christmas services are another potential hotspot. "If people are going to gather in places of worship, they should be prepared for the fact that they will be exposed to the variant," Perry Halkitis, dean of the Rutgers School of Public Health, told NorthJersey.com on Thursday. Experts' advice: Attend services virtually if possible, and wear a mask if you attend in person.

Since the beginning of the pandemic, experts have warned that eating in a restaurant poses a major COVID risk. This still applies amid the surge of the highly contagious Delta and Omicron variants. "If you're indoors whether it's a restaurant, a gym or a concert you're going to be more prone to acquiring the virus, whether you're vaccinated or not, just from that unventilated setting," Ravina Kullar, a Los Angeles-based infectious disease expert and member of the Infectious Diseases Society of America, told CNBC.

RELATED: The Supplements Doctors Say to Stop Taking Now

The Centers for Disease Control and Prevention is advising Americans not to travel to countries at Level 4 COVID spread, defined as more than 500 new COVID-19 cases per 100,000 residents in the past 28 days. More than 85 countries are now on that list, including the United Kingdom, France and Spain. You can see the agency's latest recommendations in map form here.

RELATED: The #1 Cause of "Too Much" Visceral Fat

Follow the fundamentals and help end this pandemic, no matter where you liveget vaccinated ASAP; if you live in an area with low vaccination rates, wear an N95 face mask, don't travel, social distance, avoid large crowds, don't go indoors with people you're not sheltering with (especially in bars), practice good hand hygiene, and to protect your life and the lives of others, don't visit any of these 35 Places You're Most Likely to Catch COVID.

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Places "Swarming" With COVID, Say Experts Eat This Not That - Eat This, Not That

Muting notifications might feel a bit uncomfortable: What if you miss something important? But most everyone I spoke to said something similar about this worry: The people who need to get to you will know how, whether it be via text or phone call. Your mental health and attention will thank you.

Celebrate Digital Cleanup January.If youre feeling ambitious, take a page from my colleague Tate Ryan-Mosley,a reporter ondigital rights and democracy.She will be celebrating her fourthannual Digital Cleanup January, where she devotes four weeks to cleaning up each part of her digital life: emails, files, security, and phone.

Heres how it works:

InWeek1, Tate does a massive purge of her email, unsubscribingfromnewsletters and other lists that dont serve her andmass-deleting emails she wont ever read. She also spends a day reaching out to people who might have emailed her and who she has yet to respond to. The new year is a nice time to revive those connections and lets Tate start fresh conversations with people she cares about.

Week 2is devoted to file organization: cleaning up filesinthe cloud, onthedesktop, and in any drives and putting them where they belong. Its my least favorite week, Tate says. But at the end of it, you feel like you really accomplished something. Tates advice? Dont organize files by date,but rather by general category. And treat file organization as real work,because it is. Ill do it in breaks at work if Im waiting for a meeting,or set aside an hour and listen to music and really do it, she says.

Week 3of Tates digital cleanup is devoted to security.Shegoes through each sensitive personal account and creates new unique passwords with the helpof thepassword manager LastPass. Tate also uses this week to Google herself to get rid of sensitive information,like her personal phone number and address,that might be floating around the internet. Tate swears by the New York Times guide to doxxing yourself,available here, which offers clear instructions on how to keep your private information safe online.

Week 4is the most fun, according to Tate. She takes this week to clean up her phones backlog of photos, delete apps that dont serve her, and reorganizethehome screen. The nice thing is that I dont have to be at my desk to do this, she says. I might bewaiting in line or watching TV. Tate also takes the time this week to turn off her notifications (see above).

For Tate, Digital Cleanup January isnt necessarily fun. How many resolutions are? But when the calendar turns to February, shes achieved a ton. I feel so good for the rest oftheyear, she says. And by December, I cant wait to take care of all of this again. I love how I feel afterwards.

Lastly, remember theres a whole world outside of tech.Once upon a time, people didnt crane their necks over their phones, practicing that particular thumb flick of endlessly scrollingsocial media. Some read books. Others chatted with those around themor simply zoned out for a bit.

Cal Newport, a professor of computer science at Georgetown University, advocates heavily for reforming your relationship with technology, particularly when its really not necessary. When you deploy tech toward things that are important,itshelpful, he says. When you use it as a default distraction from unpleasant thoughts or experiences, it can become a problem. So put the phone down and feel those emotions, even if theyre boredom, sadness, or anxiety. It might make you feel a bit more human again.

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Have a better 2022 with these tech resolutions - MIT Technology Review

Computer vision systems sometimes make inferences about a scene that fly in the face of common sense. For example, if a robot were processing a scene of a dinner table, it might completely ignore a bowl that is visible to any human observer, estimate that a plate is floating above the table, or misperceive a fork to be penetrating a bowl rather than leaning against it.

Move that computer vision system to a self-driving car and the stakes become much higher for example, such systems have failed to detect emergency vehicles and pedestrians crossing the street.

To overcome these errors, MIT researchers have developed a framework that helps machines see the world more like humans do reports MIT News. Their new artificial intelligence system for analyzing scenes learns to perceive real-world objects from just a few images, and perceives scenes in terms of these learned objects.

The researchers built the framework using probabilistic programming, an AI approach that enables the system to cross-check detected objects against input data, to see if the images recorded from a camera are a likely match to any candidate scene. Probabilistic inference allows the system to infer whether mismatches are likely due to noise or to errors in the scene interpretation that need to be corrected by further processing.

This common-sense safeguard allows the system to detect and correct many errors that plague the deep-learning approaches that have also been used for computer vision. Probabilistic programming also makes it possible to infer probable contact relationships between objects in the scene, and use common-sense reasoning about these contacts to infer more accurate positions for objects.

If you dont know about the contact relationships, then you could say that an object is floating above the table that would be a valid explanation. As humans, it is obvious to us that this is physically unrealistic and the object resting on top of the table is a more likely pose of the object. Because our reasoning system is aware of this sort of knowledge, it can infer more accurate poses. That is a key insight of this work, says lead author Nishad Gothoskar, an electrical engineering and computer science (EECS) PhD student with the Probabilistic Computing Project.

In addition to improving the safety of self-driving cars, this work could enhance the performance of computer perception systems that must interpret complicated arrangements of objects, like a robot tasked with cleaning a cluttered kitchen.

Gothoskars co-authors include recent EECS PhD graduate Marco Cusumano-Towner; research engineer Ben Zinberg; visiting student Matin Ghavamizadeh; Falk Pollok, a software engineer in the MIT-IBM Watson AI Lab; recent EECS masters graduate Austin Garrett; Dan Gutfreund, a principal investigator in the MIT-IBM Watson AI Lab; Joshua B. Tenenbaum, the Paul E. Newton Career Development Professor of Cognitive Science and Computation in the Department of Brain and Cognitive Sciences (BCS) and a member of the Computer Science and Artificial Intelligence Laboratory; and senior author Vikash K. Mansinghka, principal research scientist and leader of the Probabilistic Computing Project in BCS. The research is being presented at the Conference on Neural Information Processing Systems in December.

A blast from the past

To develop the system, called 3D Scene Perception via Probabilistic Programming (3DP3), the researchers drew on a concept from the early days of AI research, which is that computer vision can be thought of as the inverse of computer graphics.

Computer graphics focuses on generating images based on the representation of a scene; computer vision can be seen as the inverse of this process.Gothoskar and his collaborators made this technique more learnable and scalable by incorporating it into a framework built using probabilistic programming.

Probabilistic programming allows us to write down our knowledge about some aspects of the world in a way a computer can interpret, but at the same time, it allows us to express what we dont know, the uncertainty. So, the system is able to automatically learn from data and also automatically detect when the rules dont hold, Cusumano-Towner explains.

In this case, the model is encoded with prior knowledge about 3D scenes. For instance, 3DP3 knows that scenes are composed of different objects, and that these objects often lay flat on top of each other but they may not always be in such simple relationships. This enables the model to reason about a scene with more common sense.

Learning shapes and scenes

To analyze an image of a scene, 3DP3 first learns about the objects in that scene. After being shown only five images of an object, each taken from a different angle, 3DP3 learns the objects shape and estimates the volume it would occupy in space.

If I show you an object from five different perspectives, you can build a pretty good representation of that object. Youd understand its color, its shape, and youd be able to recognize that object in many different scenes, Gothoskar says.

Mansinghka adds, This is way less data than deep-learning approaches. For example, the Dense Fusion neural object detection system requires thousands of training examples for each object type. In contrast, 3DP3 only requires a few images per object, and reports uncertainty about the parts of each objects shape that it doesnt know.

The 3DP3 system generates a graph to represent the scene, where each object is a node and the lines that connect the nodes indicate which objects are in contact with one another. This enables 3DP3 to produce a more accurate estimation of how the objects are arranged. (Deep-learning approaches rely on depth images to estimate object poses, but these methods dont produce a graph structure of contact relationships, so their estimations are less accurate.)

Outperforming baseline models

The researchers compared 3DP3 with several deep-learning systems, all tasked with estimating the poses of 3D objects in a scene.

In nearly all instances, 3DP3 generated more accurate poses than other models and performed far better when some objects were partially obstructing others. And 3DP3 only needed to see five images of each object, while each of the baseline models it outperformed needed thousands of images for training.

When used in conjunction with another model, 3DP3 was able to improve its accuracy. For instance, a deep-learning model might predict that a bowl is floating slightly above a table, but because 3DP3 has knowledge of the contact relationships and can see that this is an unlikely configuration, it is able to make a correction by aligning the bowl with the table.

I found it surprising to see how large the errors from deep learning could sometimes be producing scene representations where objects really didnt match with what people would perceive. I also found it surprising that only a little bit of model-based inference in our causal probabilistic program was enough to detect and fix these errors. Of course, there is still a long way to go to make it fast and robust enough for challenging real-time vision systems but for the first time, were seeing probabilistic programming and structured causal models improving robustness over deep learning on hard 3D vision benchmarks, Mansinghka says.

In the future, the researchers would like to push the system further so it can learn about an object from a single image, or a single frame in a movie, and then be able to detect that object robustly in different scenes. They would also like to explore the use of 3DP3 to gather training data for a neural network. It is often difficult for humans to manually label images with 3D geometry, so 3DP3 could be used to generate more complex image labels.

The 3DP3 system combines low-fidelity graphics modeling with common-sense reasoning to correct large scene interpretation errors made by deep learning neural nets. This type of approach could have broad applicability as it addresses important failure modes of deep learning. The MIT researchers accomplishment also shows how probabilistic programming technology previously developed under DARPAs Probabilistic Programming for Advancing Machine Learning (PPAML) program can be applied to solve central problems of common-sense AI under DARPAs current Machine Common Sense (MCS) program, says Matt Turek, DARPA Program Manager for the Machine Common Sense Program, who was not involved in this research, though the program partially funded the study.

Additional funders include the Singapore Defense Science and Technology Agency collaboration with the MIT Schwarzman College of Computing, Intels Probabilistic Computing Center, the MIT-IBM Watson AI Lab, the Aphorism Foundation, and the Siegel Family Foundation.

Republished with permission ofMIT News. Read theoriginal article.

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Machines that see the world more like humans do - Big Think