Monthly Archives: April 2020

April10, 20205 min read

Opinions expressed by Entrepreneur contributors are their own.

In only a few weeks, the COVID-19 pandemic has completely disrupted our normal way of life. With many businesses shutting their doors or transitioning to a work-from-home system, adaptability to a constantly changing situation will prove key for the survival of organizations large and small. Despite everything that is going on, however, the pandemic is also spurring new innovations, particularly in the world of artificial intelligence. Here are several important ways AI is already making a difference in improvingpublic health and safety as the world adapts to a new normal.

One of the biggest challenges with this coronavirus (and the COVID-19 disease it subsequenly causes) has been how quickly it can spread. While social-distancing measures and the closure of high-risk facilities are viewed as the best way to control the spread, many areas have been slow to enact such measures because they dont have an accurate perception of their risk.

In Israel, however, an AI-powered survey system developed by the Weizmann Institute of Science aims to better predict outbreaks so authorities can proactively enact measures that will mitigate the viruss spread. The system uses a questionnaire focusing on key issues like health symptoms and isolation practices, then matches responses with a location-based algorithm. AI analysis can then identify potential hotspots in advance, which can help local authorities enact measures that will slow down the virus.

With COVID-19 constantly dominating headlines, it should come as no surprise that hospitals and health organizations are getting more inquiries than ever from patients worried that they might have the coronavirus.

Virtual assistants have already alleviated the workloads of customer support professionals in other industries, and now, similar tools specifically designed to address questions related to COVID-19 are being introduced. These AI tools can be embedded directly into healthcare apps and websites.

One example of this is Hyro, a free COVID-19 virtual assistant that is being offered to healthcare organizations to help them manage the uptick in calls and questions. By answering frequently asked questions about the coronavirus, triaging symptoms and delivering information from verified sources like the WHO and CDC, such AI tools can help reduce the burden on healthcare workers who are already being stretched thin by pandemic conditions.

Related: How Artificial Intelligence Is Helping Fight the COVID-19 Pandemic

An unfortunate issue that has popped up in the wake of the COVID-19 pandemic is the rapid spread of misinformation online. From downplaying the risks posed by the virus to false text messages warning of mandatory quarantine orders, this can further fuel panic during what is already a scary time.

Many social media platforms use human content moderators to check for harmful posts, but with more employees being required to work from home or stop working altogether, AI is becoming increasingly important in combating misinformation. Though the lack of human supervision means an increased risk for mistakes, it could also spur new improvements for these machine-learning tools.

As one example of this, The Verges Jacob Kastrenakes explains, YouTube will rely more on AI to moderate videos during the coronavirus pandemic, since many of its human reviewers are being sent home to limit the spread of the virus. This means videos may be taken down from the site purely because theyre flagged by AI as potentially violating a policy, whereas the videos might normally get routed to a human reviewer to confirm that they should be taken down.

As noted by the Guardian, one of the biggest challenges in containing the spread of COVID-19 is the fact that many patients experience symptoms most similar to a mild cold. Some are entirely asymptomatic. Because of this, many people who could spread the virus to others may continue to go out in public rather than self-quarantining.

While testing can be slow, AI is already stepping up to the challenge. As reported by The Next Web, several AI tools have already been developed to identify patients with COVID-19 and deliver treatment that keeps healthcare professionals safe.

In China, a computer-vision algorithm was developed to scan peoples temperatures in public locations and flag anyone with even a slight fever. Another AI algorithm helps doctors more accurately discern between coronavirus and typical pneumonia patients. In Washington State, robots have even been used to provide remote treatment and communication to keep the disease from spreading from patients to doctors.

Related: How Businesses Should Handle the Coronavirus Outbreak

The future surrounding the COVID-19 pandemic is rife with uncertainty. There is no telling how long social isolation measures and other precautions will need to remain in place to mitigate the spread of the disease, or what the overall impact of such actions will be.

While AI may not have all the answers, it is clear that continuing innovation in this field will help and already is helping to make the world a safer place during these troubling times. By helping slow the spread of the virus and improving conditions for healthcare workers, these tech developments could very well save lives now and in the future.

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4 Ways AI Is Making the World a Safer Place - Entrepreneur

TensTorrent founder and CEO Ljubisa Bajic talking remotely at the Linley Group Spring Processor Forum.

2016 was an amazing year in the history of computing. That year, numerous experienced computer chip designers set out on their own to design novel kinds of parts to improve the performance of artificial intelligence.

It's taken a few years, but the world is finally seeing what those young hopefuls have been working on. The new chips coming out suggest, as ZDNet has reported in past, that AI is totally changing the nature of computing. It also suggests that changes in computing are going to have an effect on how artificial intelligence programs, such as deep learning neural networks, are designed.

Case in point, startup Tenstorrent, founded in 2016 and headquartered in Toronto, Canada, on Thursday unveiled its first chip, "Grayskull," at a microprocessor conference run by the legendary computer chip analysis firm The Linley Group. The "Spring Processor Conference" was originally going to be held in Silicon Valley, but COVID-19 turned it into a Zoom video affair. The event was very well attended, organizer Linley Gwennap told ZDNet, and interest was evident from the number of audience questions submitted via Zoom chats.

Tenstorrent was founded by chief executive Ljubisa Bajic, who previously worked for Nvidia and Advanced Micro Devices, among others. As Bajic related to ZDNet, in 2016 he was trying to figure out his next move after several years at those large chip vendors. A legendary chip designer, Jim Keller, who worked with Bajic at AMD, "told me to just go and do what interested me," said Bajic. Keller wrote a check to Bajic, providing the first funding for the company.

The company has now received a total of $34 million in funding from Eclipse Ventures and Real Ventures, among others. The company also has offices in Austin, Texas and in Silicon Valley.

Bajic, and other chip teams, are responding to the explosion in the size of deep learning models, such as BERT, and OpenAI's "GPT2," but also even newer models such as Nvidia's "Megatron," Microsoft's "Turing NLG," and neural net models that Bajic said he couldn't talk about publicly that will have on the order of one-trillion parameters.

The Grayskull chip is meant to be used to speed up what's called "inference," the part of AI where a trained neural network makes predictions from new data. This part of the market has traditionally been dominated by Intel microprocessors in server computers in data centers. But Nvidia has made big inroads into inference with its graphics processing units (GPUs), and numerous startup companies have announced chip designs to compete with both of those chip giants.

The chip is expected to go into production this fall, Bajic told the conference.

The only way to beat Nvidia is with vastly superior performance, Bajic told ZDNet in an interview. "Most customers are not going to switch off of Nvidia for a part that is only two times better that is basically still an engineering sample," said Bajic. "Our goal is if we can be more than ten times better than Nvidia and sustain that for a few years, then we think people will come around to us if we can achieve that."

Early results look good. In a review of the Grayskull part put out Thursday, the Linley Group's lead analyst. Linley Gwennap, writes that the Grayskull part has "excellent" performance relative to Nvidia and other startups, including a band of former Google engineers named Groq. In fact, the chip is more efficient at performing standard AI tasks than all other chips on the market, including Nvidia's, leaving aside processors that Chinese search giant Alibaba uses internally. For example, the chip can perform 368 trillion "operations per second" on a circuit board consuming just 75 watts versus parts from Nvidia and others that require 300 watts on average. (Subscription required to read Linley Group articles.)

The "Grayskull" 75-watt PCIe card.

What's going on in the Grayskull chip has interesting implications for computing. One focus is lots and lots of computer memory. The Grayskull part has 120 megabytes of on-chip SRAM memory, compared to just 18 megabytes for Nvidia's "Titan RTX" part. Nvidia's approach has been to hook up its GPUs to the fastest off-chip memory. But Tenstorrent and other startups are increasingly emphasizing the role of faster on-chip memory.

For example, Groq's "TSP" chip has almost twice as much memory as Tenstorrent, at 220 megabytes. And the record for on-chip memory is held by Cerebras Systems, which also presented at Thursday's conference. Cerebras's part, the world's largest chip, called the "Wafer Scale Engine," which was unveiled in August, has a grand total of 18 gigabytes of on-chip memory.

The proliferation of memory as a larger and larger influence in the design of the chip has some startling implications for computer system design. For example, Groq's TSP has no DRAM interface. Instead, there are several connectors around the edges of the chip that are called "SERDES" that are the kind of connectors that are used in data networking. The idea, explained Dennis Abts, who spoke following Bajic, is that instead of adding external DRAM, one can combine multiple Groq chips together through the SERDES, so that all memory operations are handled by multiplying the available on-chip SRAM, with no DRAM whatsoever. Like Cerebras and Tenstorrent, and other companies such as Graphcore, the ultimate vision for Groq is that people will use many of its chips in massively parallel computers that are combine multiple boards together. Hence, the era of external DRAM may be drawing to a close, replaced by on-chip SRAM in massively parallel computers.

As far as speeding up AI, having lots of on-chip memory fulfills a bunch of functions. One is to keep memory close to the multiple on-chip computing cores. Tenstorrent has 120 computing cores on Grayskull, and Cerebras has 400,000 compute cores. The large on-chip memory is spread amongst these cores; it resides in the circuitry that is closest to the computing core, so that it takes no more than a single tick of the chip's clock for each core to access the memory it needs to read or write.

A schematic of the Grayskull chip, with its 120 on-chip compute cores.

As Cerebras's head of hardware engineering, Sean Lie, noted, "In machine learning, the weights and the activations are local, and there's low data reuse," he noted. "But the traditional memory hierarchy isn't built that way." Instead, general-purpose chips like Intel Xeon CPUs and GPUs spend a lot of time going all the way off the chip to external DRAM memory, which takes several clock cycles to access. By keeping the values that any one processing core is working on, instead of going away to off-chip DRAM, "the physics of local memory drives higher performance," said Lie.

There is a secondary efficiency, notes Lie, which is reducing the duplication that happens from using off-chip memory. GPUs "turn vector-matrix multiplies into matrix-matrix multiplies," he said, meaning, they bunch several inputs together, what's known as "batching," which is the bte noir of most deep learning scientists. "It actually changes the training of machine learning," distorting results, said Lie. "Remove the large-batch multiplier is a goal," said Lie. That point was echoed by Tenstorrent's Bajic. "No more general matrix multiplication," Bajic told the conference, "No more batching."

The kinds of neural net models that AI chips have to plan to handle, especially in the domain of natural language, are scaling to very, very large numbers of parameters, over a trillion, argues Tenstorrent founder and CEO Ljubisa Bajic.

Instead of batching, all three companies, Tenstorrent, Groq, and Cerebras, emphasize "sparsity," where individual inputs to a neural network are treated independently by individual cores on the chip. That's where the implications become very interesting for machine learning.

As they dispense with batching, Tenstorrent and the other companies are fixated on the concept of "sparsity," the notion that many neural networks can be processed more efficiently if redundant information is stripped away. Lie observed that there is "a large, untapped potential for sparsity" and that "neural networks are naturally sparse."

Tenstorrent's Bajic told the conference that sparsity is at the heart of the Grayskull chip. One of the big influences upon the chip's design is the way that the brain's neurons only fire some of the time they spike. Much of the time, those neurons are idle, consuming little power.

"Spiking neural nets are more efficient, in a sense," Bajic said. "They have conditional execution, they have branching, etc. They are not efficient in trillions of operations per watt and terabytes per second, but they have this very nice feature of implementing functionality only when needed, and as a result they have very good power efficiency."

Tenstorrent says that with a little bit of extra training, a neural network such as Google's BERT can be optimized to exit its programming tasks "early," saving on compute effort.

Hence, the Tenstorrent team has come up with a way to streamline the way a neural network is computed in silicon to be more selective, what he called "conditional execution." A neural network, including a popular natural language program, such as Google's BERT, goes through several layers of processing. It's possible to stop that neural network before it goes through all the layers, and avoid some computation, said Bajic. By "testing" if a neural network has reached a satisfactory answer mid-way through its computation, the program can be stopped early, what's known as "early exit."

That's what Bajic and team have done, designed a software program that re-trains a neural network to find the places where it might be able to stop early. "Take BERT as a pre-packaged bunch of code, Python code calling PyTorch primitives, and add a bit of code that attaches early exit," explained Bajic. "And then run a little bit of fine-tuning training, about a half hour of additional training."

The training step goes through the entire process of compute, said Bajic, but the trained model, once ready to perform inference, can stop where it reaches a sufficient prediction, and save some compute. "There will be a statement that says, if my input is high confidence, and based on existing data, it's just not going to run rest of the network." Tenstorrent customers can either run the extra half hour of training before compiling their neural networks, or the Tenstorrent software can automatically re-train the model. "You can take networks like BERT and GPT2 and run them through our black box and get all this done so you as a user do not have to assign engineers to do it, you don't have to negotiate with the machine learning team to get it done," said Bajic.

The result of tricks such as early exit can be a dramatic speed up in performance. The Grayskull chip can process 10,150 sentences per second with BERT versus the customary 2,830 sentences that most chips can process in that time.

That's a neat trick, but it's also a change to the way that neural nets are thought about. The Grayskull part signals that what a chip can do may change how such networks are designed in future. It's like what Facebook's head of AI, Yann LeCun, pointed out a year ago: "Hardware capabilities and software tools both motivate and limit the type of ideas that AI researchers will imagine and will allow themselves to pursue."

"The tools at our disposal fashion our thoughts more than we care to admit," LeCun has said.

What is coming into focus, then, is a world in which both computing approaches and artificial intelligence approaches will be changing simultaneously, affecting one another in a symbiotic way. That means it will become increasingly difficult to talk about things such as how fast a given machine learning model runs, or how fast a chip is, without considering how different both are from past efforts. Metrics in either case will be intimately bound up with the choices made by chip designers, computer designers, and AI scientists who build neural networks.

"MLPerf has not comprehended this kind of approach to the problem at all," said Bajic, referring to benchmark chip tests. "We sort of make it not quite BERT," he said. "that's something I would invite MLPerf people to think about."

See more here:

Startup Tenstorrent shows AI is changing computing and vice versa - ZDNet

McGill University researchers say theyve developed a technique to train a remote-controlled, offroad car to drive on terrain from aerial and first-person imagery. Their hybrid approach accounts for terrain roughness and obstacles using on-board sensors, enabling it to generalize to environments with vegetation, rocks, and sandy trails.

The work is preliminary, but it might hold promise for autonomous vehicle companies that rely chiefly on camera footage to train their navigational AI. U.K.-based Wayve is in that camp, as are Tesla, Mobileye, and Comma.ai.

The researchers work combines elements of model-free and model-based AI training methods into a single graph to leverage the strength of both while offsetting their weaknesses. (As opposed to model-free methods, model-based methods have a software agent try to understand the world and create a model representing it, which sometimes leads to poor performance due to cascading errors.) Their model learns to navigate collision-free trajectories while favoring smooth terrain in a self-supervised fashion, such that the training data is labeled autonomously.

The researchers off-road vehicle is based on an electric, two-motor remote-controlled buggy with a mechanical brake thats wirelessly connected to an Intel i7 NUC computer running the open source Robot Operating System (ROS). The buggy is equipped with both a short-range lidar sensor and a forward-facing camera coupled with an inertial measurement unit, and with a microcontroller that relays all sensor information to the NUC computer.

Before deploying the buggy on an all-terrain course, the team captured images of the course from an 80-meter height using a DJI Mavic Pro, and then they extracted 12-meter-by-9-meter patches of the images so that they could be oriented and centered. The images were taken at a resolution of 0.01 meters per pixel and were aligned within 0.1 meter, using four visual landmarks measured with the buggy.

During training, the teams model estimates terrain roughness using an inertial measurement unit while the lidar sensor measures the distance between obstacles. Given fused input images from an onboard camera and local aerial view, a recent visual history, terrain class labels (e.g., rough, smooth, obstacle), and a sequence of steering commands, it predicts collision probabilities over a fixed horizon from which a policy or strategy can be derived.

In a real-world field trial, the researchers had the buggy drive at a speed of 6 kilometers per hour (~3.7 miles per hour) after training on 15,000 data samples collected over 5.25 kilometers (~3.2 miles). They report that the navigational model achieved a prediction accuracy of 60% to 78% using the forward ground camera and that when the aerial imagery was incorporated, accuracy increased by around 10% for trajectories with angle changes of 45 degrees or higher. Indeed, the policy drove on smooth terrain 90% of the time and reduced the proportion of rough terrain by over 6.1 times compared with a model using only first-person imagery.

More here:

AI training helps remote-controlled buggy negotiate rugged terrain - VentureBeat

If, like many people these days, youre trying to get a firmer understanding of what AI is and how it works but are secretly panicking a little because youre struggling with terminology so opaque that youre lost before you get to Markov chains, you may want to crack open Janelle Shanes new book. She recently sat down with VentureBeat to talk about the book, whose title, You Look Like a Thing and I Love You, is actually an AI-generated pickup line.

Shane maintains the AI Weirdness blog and combines knowledge from her Ph.D. in electrical engineering, fascination with AI, and propensity for slightly deadpan absurdist humor to explain AI in a way that is both hilarious and easy to understand. More importantly, she uses humor as a frame to display how AI is actually dangerously bad at most things we wish it could do. Her take is a refreshing counter to the often overly fantastical notions about AI, its looming sentience, and its capacity for either utopia or dystopia.

Although the book walks the reader through what AI is and explains how AI thinks and how we should think about how AI thinks, its full of giggle-inducing hand-drawn illustrations and endless comical examples. Shanes signature move is using neural nets to generate things like new ice cream flavors, knitting patterns, and recipes. The results are usually funny and bizarre and are things that could almost exist, like Lemon-Oreo ice cream or a Cluckerfluffer sandwich (chicken, cheese, and marshmallow). Its the least creepy way to show how AI so often falls off a cliff into the uncanny valley.

If you can make it past the recipe for Basic Clam Frosting without laughing (page 18), there might be something wrong with you.

Above: These flavors are not delicious. Janelle Shane at her TED Talk

Image Credit: TED

This interview has been edited for length and clarity.

VentureBeat: When I first came across [the book], it seemed like it was going to be very educational. And also very accessible. And I thought that sounded fantastic. I know this all started with AI Weirdness. Why did you start that blog? How did that come about?

Janelle Shane: I saw someone else named Tom Brewe who had used one of these text-generating neural nets to do cookbook recipes. It was one of these situations where Im laughing so hard the tears are streaming down my face. I have now read all of the recipes hes generated there now needs to be more recipes! And [I thought] I need to learn how to generate them. So thats kind of it was more like, I thought this was hilarious. I generated this thing that Im like, Okay, now I have to show people. I figured it would be a few of my friends who would read it. I had the blog from my Ph.D. days that mostly had electron microscope pictures on it. So I just threw some of these experiments on there, [thinking], you know, Heres a blog, Ill just put this stuff on there. And then, yeah, to my surprise, other people started reading it, like, people I didnt even know personally.

VentureBeat: At what point did the book emerge as a thing, coming out of the blog?

Shane: In part it was because I got [to] talking to an agent and to a publisher who were interested in there being this book. But, you know, the other motivation too was, again, repeatedly getting the comments of people [who were] confused because the AI on my blog was making all sorts of mistakes and Isnt AI supposed to be smart? So this book is kind of a way to explain how these things can be true at once and what smart means in the context of AI. And what it definitely doesnt mean.

VentureBeat: So they didnt get the joke? (Which is also kind of funny.)

Shane: It was more like they were confused, like they got that, Okay, the AI is making mistakes, and haha thats funny but why is this one so bad? And, you know, is this a different kind of AI? This is stuff that I was recommending movies or labeling photos and things.

VentureBeat: And how did you come to the illustrations? Was that kind of your thing, or did the publisher kind of come in and do that?

Shane: Those are all my own illustrations.

VentureBeat: How did you come up with that sort of language its a really funny and educational sort of artistic language. How did that come to you?

Shane: This is how I explain things to myself in my own voice. I remember kind of overhearing when I was doing rehearsals, actually, for the TED conference, and I overheard a couple of the coaches saying to each other, No, she really does sound like that! So yeah, its not an act! This is really how I talk about these things and how I like to write them down for myself so they sort of make sense. Im telling myself this story to sort of put my thoughts in order.

Definitely, this approach where focusing in on examples and especially on these kind of memorable things thats whats going to stick around after youre done reading the book. Its not like my bullet-pointed principles of AI weirdness was going to stick in [your] mind as much as the story about, you know, the AI that got confused about whether were supposed to be identifying fish and whether human fingers holding the trophy fish are part of the fish. Or, you know, the AI that was told to sort a list of numbers and deleted the list, therefore eliminating all the sorting errors, thus technically getting a perfect score.

So that sort of focusing in on these really weird, very non-human sorts of things to do I think in part comes from the first way that I encountered machine learning, which was as a freshman in college in 2002, sitting in on a lecture by a guy whos studying evolutionary algorithms. His name is Professor Erik Goodman, and he was explaining his research and telling just these same kinds of stories. And I remember that just really grabbed me and was so weird and memorable and made sense, but was also difficult to picture, you know kind of like studying the natural world in a way. And so that really kind of was the way I got drawn into machine learning.

VentureBeat: It struck me that the way you explained how neural nets work is very Amelia Bedelia-like. [Amelia Bedelia is a childrens book character who takes everything people say literally, thus exposing the humor in things like idioms.] Its almost like the math version of how Amelia Bedelia plays with language.

Shane: Yeah, you know, thats one of the things thats fun about neural nets, is that they dont have the context for the exact problem theyre trying to solve. They take things entirely literally because thats all we know how to do. And then you get some really weird situations, this really weird humor that kind of arises from that. So yeah, it definitely pushes some of the same buttons.

VentureBeat: You use real-world examples, and sort of absurdist humor, to show what happens when AI goes awry. And its kind of easy to vet the output, because it kind of comes out as gibberish, right? We can tell that the recipe doesnt make sense, and its fun to make it and sort of point that out, but I wonder about less whimsical examples. Because, you know, theres a lot of researchers and practitioners who are doing the same things. And I guess the concern that I had is: I can tell this output is silly, but can they? And how are they able to vet their own results in kind of the same way that we can when we have a hilarious recipe?

Shane: Yeah, that is the thing about these kinds of experiments that kind of led me into explaining AI. When you have these absurdist, real-world examples Its not deep into the statistics of handing out loans or, you know, looking at resumes, but its messing up something that we all can kind of see. That is a really helpful way of seeing what kinds of limitations there are and how these same sorts of limitations pop up over and over again.

Theyre making these same sorts of mistakes, and the question is, are they looking at them closely enough to catch these mistakes?

VentureBeat: Are there established solutions for people to vet that output, or is that still a big problem to solve?

Shane: The only general rule I can say is, never trust a neural net. Never trust it to know what you asked it. Never trust it not to take a speedy shortcut. And then there are ways of digging in and finding out whether it has solved this correctly. That varies depending on what problem youre trying to solve.

So, in image recognition, there are some really nice tools for explainability, where [the neural net] highlights parts of the images its using to make decisions and [you can] say, Oh no, its supposed to be looking at a dog, and instead its looking at the grassy background, or something like that.

There are those kinds of tools. Some feel explainability is really hard to build in, and in some cases your main tools may be running statistics on your output and saying, Okay, we dont know how its making decisions on these loan applications. We dont know what rules its applying, but we can at least run a whole bunch of test applications. Throw them at it. See if there are any trends in the way that it makes the decision, so we know how the decision should turn out if the thing isnt biased.

I know there are services out there now systematically testing to see whether ones algorithm is biased or has some other kinds of outcomes. And I know that theres a lot of companies that just plain dont do that. You know, I would like to see there be more of a top-down requirement that these kinds of algorithms be vetted by some standardized process at least demonstrate that theyre not problematic. Or theyre not as problematic as they could be.

VentureBeat: That kind of speaks a bit to what you got into in the last couple of chapters, especially when you kind of hammered on human-faked AI: Sometimes these [AI] companies are like, We can do this, trust us, and then it turns out they cant do it, and then they kind of have to panic and either give back all the startup money because they failed or they get in there and kind of mess with it and fix it by hand. That speaks to a bit of the black-boxness of it.

Im coming from a journalist perspective, where Im not an expert. And so we have to have, you know, a certain amount of trust when someone tells us their thing works its tricky. So Im wondering, just in your opinion, how pervasive do you think that problem is in the market?

Shane: I think its pretty pervasive, actually. There are some fields that are perhaps more prone than others to these kinds of problems. I would look really closely at anybody who automatically sorts resumes. Weve seen some case studies, like this one from Amazon, where they volunteered that they had a tool they decided not to use because it was so hard to get it to stop discriminating.

Its kind of like a rare case where we get to see the struggles of the engineering team, trying to get rid of this things tendency to try to copy the bias of training. For every Amazon that tells us, Whoops, we had a problem, we decided to mitigate it by in this case not using this kind of solution at all, weve got a bunch of companies out there who are doing basically the same kind of application, and essentially the same kind of approach, and they havent published any numbers on what kind of bias they have or what specifically theyve done to reduce bias. So, yeah, theres a lot of unfounded claims out there you say, Oh yes, use our computer algorithm and we will take care of your HR teams hiring bias, without any proof that using AI is actually going to help.

VentureBeat: Pretty quickly a trend has emerged, where [makers of AI products and services] are like, Oh no, theres humans in the loop for sure. And whether its lip service or if they really believe it, theyll say, Yeah, look, we have to have the human touch, and they dont come out and say We cant trust our models. With that in mind, do you think that sometimes companies are kind of just pivoting to that messaging without really addressing the core problem?

Shane: There are definitely a lot of AI applications where human curation is needed as a kind of quality control. Definitely in all the AI-generated humor that Im doing, like most of its not interesting. You need a human. I would never train a text-generating algorithm and then just, like, give it to children to have fun with it. It is so hard to sanitize! When people talk to me about doing this kind of project, I always say, You dont want this thing talking to the public. Because it will say something terrible, and you need the human layer in between to stop that from happening.

And, you know, in so many cases, especially with some kind of creative application those are the ones I know the most about I definitely see, you know, its a time saver, but you still need a human. Language translation [is the] same sort of thing; human translators now use machine translation for the first draft. And its close, but it is definitely not ready without some human quality cleanup.

But then we have this other case, going back to having a human in the loop to try to prevent the algorithm from being biased. And thats kind of interesting; circling back to [the idea that] the humans [who built the algorithm] are biased, the algorithms biased and needs the humans. And to that I would just say, Well just, you know show me the data.

We can run test data through this thing. Thats the beauty of having an algorithm without a human running these decisions. We can give it 10,000 loans or 10,000 resumes or 10,000 social media profiles and see if there are any trends. And If someone hasnt done that, I worry that theyre not serious about whether or not their algorithm is flawed in a potentially harmful way.

VentureBeat: What do you think, if anything, has changed in the field like in terms of research, impact, deployment, whatever since you finished writing the book? (I know it was 2019, which is recent.)

Shane: Oh man, things change so quickly. One of the things Ive been encouraged to see is more pushback against some of the bad applications and more communities and people stepping up to bat against this and governments also trying to step in especially [the] European Parliament trying to step in and do the right things.

So Im encouraged that were hopefully going to be a bit out of the snake-oil realm. Therere now more applications out there to worry about, like with facial recognition. Its not great, but its working better, so there are different aspects to worry about, versus in my book, where the concern was [that] it doesnt even work. Well now it kind of works, and thats also a problem.

VentureBeat: What are your top five favorite random things generated by a neural net?

Shane: Oh man. [pause] I really like the experiments where people take the neural net stuff at the starting point and just run with it. There was an experiment I did AInktober where it was generating drawing boxes during the month of October. People drew the most amazing things.

There was one called SkyKnit, where its generating knitting patterns, and people had to try and debug all of the mistakes and make them into things, and it was just glorious.

And then Hat 3000 did the same thing with crocheting. Turns out, using crocheted hats was an unwittingly bad move that tended to create universe-devouring, hyperbolic yarn monstrosities.

There was one example I did where I was generating cookies, and people actually made recipes based on the illustrations, like spice biggers and walps and apricot dream moles.

The paint colors keep coming back again. Using paint colors gives me the opportunity to print the word TURDLY in giant letters across presentation screens.

Signed copies of the book are available from Boulder Book Store.

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Janelle Shane explains AI with weirdness and humor, in book form - VentureBeat

COVID-19 waits for no one, and the speed of its spread has forced the world to act with unprecedented haste. From world governments to individual households, everyone has suddenly had to scrap plans, make new ones, and then try to hang on to some kind of new normal as the pandemic causes more unexpected and rapid shifts.

In the tech world, watching everyone move so fast has been quite a sight. As I wrote a few weeks ago, theres been a digital flotilla of tech people focusing on, and turning their expertise toward, solving the problems related to COVID-19. But with increased speed comes increased noise, which is a mixed blessing.

Like (I presume) all tech journalists right now, my inbox is more full than usual. Its brimming with endless pitches for chatbots to help answer peoples COVID-19 questions and even triage symptoms. Theres been an explosion of apps designed to track the spread of the coronavirus. There are pitches about robots and drones and autonomous vehicles that help in hospitals and deliver supplies. Were being told about all sorts of AI tools that claim to help medical providers diagnose COVID-19. Pitches for just about any company, product, or service that could conceivably be related to remote work have come our way.

There are also uplifting pitches about how this or that company is giving away its product or service for free, adapting it for a selfless purpose to help people in the wake of job losses and health scares, or marshaling resources to perform much-needed research.

It truly is encouraging, and at times downright inspiring, to see the wealth of new tools and techniques to track, prevent, treat, and in general fight COVID-19.

But finding the best parts and pieces amidst the unrelenting noise is a daily if not hourly challenge. As with all technology, hasty execution often invites privacy issues or poor security, like those Zoom has experienced even as its daily active user numbers (DAUs) have reached the stratosphere. Companies can also suddenly bump into regulatory hurdles or interoperability issues. (Fortunately, rapid cooperation between governments, researchers, and tech companies, and even between strange bedfellows such as Apple and Google, has proven possible.)

Another challenge is separating the do-gooders from the charlatans. When is a company truly being selfless and when are they just using the pandemic to slip in some positive marketing about their widget? When is it both? Its often hard to tell and one should generally be suspicious any time a for-profit company proclaims altruism but its even more difficult to discern amid the cacophony. Of course, even if there are knock-on or hidden benefits for companies that give away valuable things for free, its hard not to be pleased with what IBM, Google, smaller companies like Element AI, and many others have done to foster research and collaboration in the fight against COVID-19.

When things change overwhelmingly quickly, its usually a sign that we need more focus but that may be impossible in this climate. Taking a broad example, the Gates Foundation is funding manufacturing for multiple potential vaccine trials at once because, Bill Gates said in an interview with Trevor Noah on The Daily Show, theres no time to evaluate which vaccine has the greatest likelihood of success. Usually, a couple of the most promising vaccines would emerge from trials and then the foundation would throw its financial support behind manufacturing the best ones. Instead, its planning to waste a few billion dollars in the name of urgency.

This is a worldwide sprint and a marathon at the same time, and it can be tough to assimilate all the necessary information or in our case, to sift through the raft of news and analysis stories that come our way. But finding the signal through the noise is a skill we all have to acquire now, because combatting this global pandemic is the greatest challenge any of us has faced, and it wont wait for us to catch up.

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AI Weekly: The sudden speed of technological change in a coronavirus world - VentureBeat

Video calls are a nice way to reconnect with people you haven't seen in a while or can no longer work face-to-face with, but they can really eat up a lot of time.

Matt Reed, an ambitious coder who would frankly rather be scrolling Reddit or working than calling into another boring Zoom meeting, found a way to cheat the system. Well, sort of.

Reed created an "AI Digital Twin" or a Zoombot if you will, and taught it speech recognition and text-to-speech conversation. Now, the fake version of himself is able to stand-in on Zoom calls and even respond to programed questions like "How are you?" and "Did you get that?" The only problem is it's very clear the Zoombot is a bot.

You can read all about how Matt built his AI twin here, and watch the video for a look at the hilarious Zoombot in action.

Originally posted here:

Man creates hilarious AI version of himself to take his spot during Zoom calls - Mashable