AI is fundamental to many products and services today, but its hunger for data and computing cycles is bottomless. Lightmatter plans to leapfrog Moore’s law with its ultra-fast photonic chips specialized for AI work, and with a new $80M round the company is poised to take its light-powered computing to market.
We first covered Lightmatter in 2018, when the founders were fresh out of MIT and had raised $11M to prove that their idea of photonic computing was as valuable as they claimed. They spent the next three years and change building and refining the tech — and running into all the hurdles that hardware startups and technical founders tend to find.
For a full breakdown of what the company’s tech does, read that feature — the essentials haven’t changed.
In a nutshell, Lightmatter’s chips perform certain complex calculations fundamental to machine learning in a flash — literally. Instead of using charge, logic gates, and transistors to record and manipulate data, the chips use photonic circuits that perform the calculations by manipulating the path of light. It’s been possible for years, but until recently getting it to work at scale, and for a practical, indeed a highly valuable purpose has not.
Prototype to product
It wasn’t entirely clear in 2018 when Lightmatter was getting off the ground whether this tech would be something they could sell to replace more traditional compute clusters like the thousands of custom units companies like Google and Amazon use to train their AIs.
“We knew in principle the tech should be great, but there were a lot of details we needed to figure out,” CEO and co-founder Nick Harris told TechCrunch in an interview. “Lots of hard theoretical computer science and chip design challenges we needed to overcome… and COVID was a beast.”
With suppliers out of commission and many in the industry pausing partnerships, delaying projects, and other things, the pandemic put Lightmatter months behind schedule, but they came out the other side stronger. Harris said that the challenges of building a chip company from the ground up were substantial, if not unexpected.
“In general what we’re doing is pretty crazy,” he admitted. “We’re building computers from nothing. We design the chip, the chip package, the card the chip package sits on, the system the cards go in, and the software that runs on it…. we’ve had to build a company that straddles all this expertise.”
That company has grown from its handful of founders to more than 70 employees in Mountain View and Boston, and the growth will continue as it brings its new product to market.
Where a few years ago Lightmatter’s product was more of a well-informed twinkle in the eye, now it has taken a more solid form in the Envise, which they call a ‘general purpose photonic AI accelerator.” It’s a server unit designed to fit into normal datacenter racks but equipped with multiple photonic computing units, which can perform neural network inference processes at mind-boggling speeds. (It’s limited to certain types of calculations, namely linear algebra for now, and not complex logic, but this type of math happens to be a major component of machine learning processes.)
Harris was reticent to provide exact numbers on performance improvements, but more because those improvements are increasing than that they’re not impressive enough. The website suggests it’s 5x faster than an NVIDIA A100 unit on a large transformer model like BERT, while using about 15 percent of the energy. That makes the platform doubly attractive to deep-pocketed AI giants like Google and Amazon, which constantly require both more computing power and who pay through the nose for the energy required to use it. Either better performance or lower energy cost would be great — both together is irresistible.
It’s Lightmatter’s initial plan to test these units with its most likely customers by the end of 2021, refining it and bringing it up to production levels so it can be sold widely. But Harris emphasized this was essentially the Model T of their new approach.
“If we’re right, we just invented the next transistor,” he said, and for the purposes of large-scale computing, the claim is not without merit. You’re not going to have a miniature photonic computer in your hand any time soon, but in datacenters, where as much as 10 percent of the world’s power is predicted to go by 2030, “they really have unlimited appetite.”
The color of math
There are two main ways by which Lightmatter plans to improve the capabilities of its photonic computers. The first, and most insane sounding, is processing in different colors.
It’s not so wild when you think about how these computers actually work. Transistors, which have been at the heart of computing for decades, use electricity to perform logic operations, opening and closing gates and so on. At a macro scale you can have different frequencies of electricity that can be manipulated like waveforms, but at this smaller scale it doesn’t work like that. You just have one form of currency, electrons, and gates are either open or closed.
In Lightmatter’s devices, however, light passes through waveguides that perform the calculations as it goes, simplifying (in some ways) and speeding up the process. And light, as we all learned in science class, comes in a variety of wavelengths — all of which can be used independently and simultaneously on the same hardware.
The same optical magic that lets a signal sent from a blue laser be processed at the speed of light works for a red or a green laser with minimal modification. And if the light waves don’t interfere with one another, they can travel through the same optical components at the same time without losing any coherence.
That means that if a Lightmatter chip can do, say, a million calculations a second using a red laser source, adding another color doubles that to two million, adding another makes three — with very little in the way of modification needed. The chief obstacle is getting lasers that are up to the task, Harris said. Being able to take roughly the same hardware and near-instantly double, triple, or 20x the performance makes for a nice roadmap.
It also leads to the second challenge the company is working on clearing away, namely interconnect. Any supercomputer is composed of many small individual computers, thousands and thousands of them, working in perfect synchrony. In order for them to do so, they need to communicate constantly to make sure each core knows what other cores are doing, and otherwise coordinate the immensely complex computing problems supercomputing is designed to take on. (Intel talks about this “concurrency” problem building an exa-scale supercomputer here.)
“One of the things we’ve learned along the way is, how do you get these chips to talk to each other when they get to the point where they’re so fast that they’re just sitting there waiting most of the time?” said Harris. The Lightmatter chips are doing work so quickly that they can’t rely on traditional computing cores to coordinate between them.
A photonic problem, it seems, requires a photonic solution: a wafer-scale interconnect board that uses waveguides instead of fiber optics to transfer data between the different cores. Fiber connections aren’t exactly slow, of course, but they aren’t infinitely fast, and the fibers themselves are actually fairly bulky at the scales chips are designed, limiting the number of channels you can have between cores.
“We built the optics, the waveguides, into the chip itself; we can fit 40 waveguides into the space of a single optical fiber,” said Harris. “That means you have way more lanes operating in parallel — it gets you to absurdly high interconnect speeds.” (Chip and server fiends can find that specs here.)
The optical interconnect board is called Passage, and will be part of a future generation of its Envise products — but as with the color calculation, it’s for a future generation. 5-10x performance at a fraction of the power will have to satisfy their potential customers for the present.
Putting that $80M to work
Those customers, initially the “hyper-scale” data handlers that already own datacenters and supercomputers that they’re maxing out, will be getting the first test chips later this year. That’s where the B round is primarily going, Harris said: “We’re funding our early access program.”
That means both building hardware to ship (very expensive per unit before economies of scale kick in, not to mention the present difficulties with suppliers) and building the go-to-market team. Servicing, support, and the immense amount of software that goes along with something like this — there’s a lot of hiring going on.
The round itself was led by Viking Global Investors, with participation from HP Enterprise, Lockheed Martin, SIP Global Partners, and previous investors GV, Matrix Partners and Spark Capital. It brings their total raised to about $113 million; There was the initial $11M A round, then GV hopping on with a $22M A-1, then this $80M.
Although there are other companies pursuing photonic computing and its potential applications in neural networks especially, Harris didn’t seem to feel that they were nipping at Lightmatter’s heels. Few if any seem close to shipping a product, and at any rate this is a market that is in the middle of its hockey stick moment. He pointed to an OpenAI study indicating that the demand for AI-related computing is increasing far faster than existing technology can provide it, except with ever larger datacenters.
The next decade will bring economic and political pressure to rein in that power consumption, just as we’ve seen with the cryptocurrency world, and Lightmatter is poised and ready to provide an efficient, powerful alternative to the usual GPU-based fare.
As Harris suggested hopefully earlier, what his company has made is potentially transformative in the industry and if so there’s no hurry — if there’s a gold rush, they’ve already staked their claim.
Usage of AI for Customer Behavior Analysis
The world is getting revolutionized with the impact of technologies like Artificial Intelligence and machine learning. These digital technologies have impacted various sectors and became valuable to humankind.
Artificial intelligence is the technology that has created a revolution in the business sector by offering advanced solutions and tactics for the effective growth of the business. Business people are keen on applying such digital solutions where they can take advantage of AI. They are getting digitally equipped by having an online platform through which they can run a business online as well as take advantage of AI and other such technologies.
“The fascination of AI has been disrupting the business sector like never before and rightly so because of the wings it offers to them to fly.”
In recent times, from small businesses to large scale, everyone is introducing digitization to their business to go on the digital floors. The online business concept has been a crucial turnaround in the business sector and elevated the business scope. Whether it is small grocers who build grocery apps to serve their audiences online or the supermarkets that run business digitally and advancing their consumer service, all of them are taking healthy advantage of digital solutions.
The adoption of digital solutions opens up the doors of innovation for business people to grow their business with concepts like AI. Artificial intelligence is the latest trend which business people are focusing on, and the variations it offers are impeccable.
One of the outstanding uses of AI in the business world is customer service. It can be a very valuable integration because it is the concept that can be used for key analysis on customer behaviour and offering an outstanding experience to consumers.
AI In Customer Behaviour Analysis
If you want to succeed in the business world, you need to acknowledge your customers and target audiences well enough. The more you know about them, the more you have a chance to succeed, and that is because you will be able to offer better service to them as you already have insights about them.
“The integration of AI gives the liberty of customer behaviour analysis to the business model, which can be very helpful for the growth of the business. “
Businesspeople used to do customer behaviour analysis before, but previously due to lack of technologies, it was way more time-consuming. With concepts like Artificial intelligence, things have become more accessible. It is the concept that helps in analyzing the customer behaviour deeply so that you can take proper actions to offer amazing services to customers and eventually reduce the churn rate.
Aspects Of Customer Behaviour Analysis With AI
Customer behaviour analysis can be a better sight for achieving business growth, and to comfortably execute it AI is there for you. Businesses need to deal with customer expectations, preferences, satisfaction, wishes, etc., and if they succeed in this, the customers will be happy. Thus, intelligent analysis is required to clear all the aspects in offering the best service.
Understanding Purchasing Habits
Artificial intelligence will allow business people to know and understand the purchasing habits of potential or current customers. AI records all the points of interaction with customers for deep analysis and extracting the best meaningful outcome from it. Artificial intelligence analyses the customer’s purchase habits by having records of data that has been collected.
Knowing Customer Expectations
Fulfilling customer expectations is very important in the business world. If you understand what customers are expecting in terms of product quality, quantity, tastes, use, prices, etc., and you are successfully delivering it to them. In that case, that is the best thing happening to your business. AI-powered businesses have the advantage of understanding customer expectations through various customer behaviour analyses.
Forecasting Customer Needs
Forecasting customer needs can be very advantageous, and artificial intelligence is here for you to analyze customers’ future needs. Different customers have their individual requirements, and with AI, personalized approaches can be provided. The analysis of customer behaviour will allow them to forecast the needs of customers, and it will help in recommending the products and services in advance, which will be very effective in increasing sales.
How AI Does Customer Behaviour Analysis?
Artificial intelligence is the technology that records information about customers’ sentiments when they surf the internet world. AI collects the requisite information and extracts meaningful insight from it, which helps understand customer behaviour through analysis. The insights like buying preferences and frequencies are revealed with the help of Artificial intelligence.
Reduction In Churn Rate With AI
The customer behaviour analysis will elevate the customer experiences as business people will take some actions depending on the output of the analysis. The churn rate is something that business people need to reduce to grow their business. Artificial intelligence will help business people to improve their churn rate because of customer behaviour analysis.
- Empower business as AI drives better customer service
- Greater customer experience by understanding customer behaviour.
- Hence, betterment in customer service will reduce the churn rate.
Due to the analysis, the businesses will be able to offer better experiences to customers by improving customer service. The betterment in the customer service will have a good impact on the business model, and customers are likely to enjoy it, which will increase customer retention. Thus, businesses will not lose customers, and their churn rate will not be reduced.
Cultural Shift In Business World Must Occur
The business sector is still in the initial stage of integrating such advanced technologies as Artificial intelligence. According to Forbes, only 23% of current business respondents use AI for running their business. Business people need to understand the importance of AI and other such technologies. That is how the cultural shift in the business sector will happen, which will have more room for advanced technologies.
The main reason behind business people not actively supporting integrating the technologies is the realization of their importance. Another important reason is that they believe in hype that these technological solutions are very complex. If the efforts are put in integrating such, it offers a great outcome for them. Thus, the realization of the importance will lead the path of the cultural shift in the business sector.
“The technological shift in the business sector will open new doors of innovation and discover the new path of success”
Artificial intelligence can create a crucial turnaround in the entire sector because of its verticals. Customer behavior analysis being the most advantageous factor of integrating AI in the retail sector. Thus, the scope of AI in business is wide and fruitful.
The use of AI in customer behaviour analysis will greatly impact the ventures and give it better scope for succeeding. The analytical solutions it offers are outstanding. There are major benefits to the business if they are able to understand and predict their customers’ behaviour regarding their products and services.
The current industry giants are already using AI and have great benefits in getting popularity. The rise in overall business standards is the excellent advantage of integrating technology solutions. Thus, with time and room, AI will continue to disrupt the commercial sector with its wings.
— Brijesh is the tech activist, blogger, and internet marketing officer of Elluminati Inc
Nvidia’s Canvas AI painting tool instantly turns blobs into realistic landscapes
AI has been filling in the gaps for illustrators and photographers for years now — literally, it intelligently fills gaps with visual content. But the latest tools are aimed at letting an AI give artists a hand from the earliest, blank-canvas stages of a piece. Nvidia’s new Canvas tool lets the creator rough in a landscape like paint-by-numbers blobs, then fills it in with convincingly photorealistic (if not quite gallery-ready) content.
Each distinct color represents a different type of feature: mountains, water, grass, ruins, etc. When colors are blobbed onto the canvas, the crude sketch is passed to a generative adversarial network. GANs essentially pass content back and forth between a creator AI that tries to make (in this case) a realistic image and a detector AI that evaluates how realistic that image is. These work together to make what they think is a fairly realistic depiction of what’s been suggested.
It’s pretty much a more user-friendly version of the prototype GauGAN (get it?) shown at CVPR in 2019. This one is much smoother around the edges, produces better imagery, and can run on any Windows computer with a decent Nvidia graphics card.
This method has been used to create very realistic faces, animals and landscapes, though there’s usually some kind of “tell” that a human can spot. But the Canvas app isn’t trying to make something indistinguishable from reality — as concept artist Jama Jurabaev explains in the video below, it’s more about being able to experiment freely with imagery more detailed than a doodle.
For instance, if you want to have a moldering ruin in a field with a river off to one side, a quick pencil sketch can only tell you so much about what the final piece might look like. What if you have it one way in your head, and then two hours of painting and coloring later you realize that because the sun is setting on the left side of the painting, it makes the shadows awkward in the foreground?
If instead you just scribbled these features into Canvas, you might see that this was the case right away, and move on to the next idea. There are even ways to quickly change the time of day, palette, and other high-level parameters so they can quickly be evaluated as options.
“I’m not afraid of blank canvas any more,” said Jurabaev. “I’m not afraid to make very big changes, because I know there’s always AI helping me out with details… I can put all my effort into the creative side of things, and I’ll let Canvas handle the rest.”
It’s very like Google’s Chimera Painter, if you remember that particular nightmare fuel, in which an almost identical process was used to create fantastic animals. Instead of snow, rock and bushes, it had hind leg, fur, teeth and so on, which made it rather more complicated to use and easy to go wrong with.
Still, it may be better than the alternative, for certainly an amateur like myself could never draw even the weird tube-like animals that resulted from basic blob painting.
Unlike the Chimera Creator, however, this app is run locally, and requires a beefy Nvidia video card to do it. GPUs have long been the hardware of choice for machine learning applications, and something like a real-time GAN definitely needs a chunky one. You can download the app for free here.
How one founder realized satellite internet didn’t have to be fast or expensive to be useful
It’s hard to understand just how steeply the cost of launching and operating satellites has dropped, particularly since the introduction of lower cost launch services from a number of commercial players, and the maturation of the smartphone supply chain. Swarm co-founder and CEO realized just how much the cost curve had changed when she and her co-founder Ben Longmeir realized that they could outfit tiny satellites Longmeir had created as a kind of space lover’s hobby with the equipment needed to provide low-bandwidth connectivity to low-powered devices around the world.
In this week’s episode of Found, Sara walks us through how she went from an engineering career that included stints at NASA’s Jet Propulsion Laboratory and Google, to building Swarm as a first-time founder and CEO. We covered a range of topics including how Sara and Ben decided who would be CEO, what it’s like leading a small but growing team, and how to evaluate your decisions as a founder, and commit to a course of action to move forward.
Sara was extremely candid with us about her experience as a founder and CEO, and this is definitely one of our most open and honest conversations to date.
We loved our time chatting with Sara, and we hope you love yours listening to the episode. And of course, we’d love if you can subscribe to Found in Apple Podcasts, on Spotify, on Google Podcasts or in your podcast app of choice. Please leave us a review and let us know what you think, or send us direct feedback either on Twitter or via email at firstname.lastname@example.org. And please join us again next week for our next featured founder.
As clinical guidelines shift, heart disease screening startup pulls in $43M Series B
Cleerly Coronary, a company that uses A.I powered imaging to analyze heart scans, announced a $43 million Series B funding this week. The funding comes at a moment when it seems that a new way of screening for heart disease is on its way.
Cleerly was started in 2017 by James K. Min a cardiologist, and the director of the Dalio Institute for Cardiac Imaging at New York Presbyterian Hospital/Weill Cornell Medical College. The company, which uses A.I to analyze detailed CT scans of the heart, has 60 employees, and has raised $54 million in total funding.
The Series B round was led by Vensana Capital, but also included LVR Health, New Leaf Venture Partners, DigiTx Partners, and Cigna Ventures.
The startup’s aim is to provide analysis of detailed pictures of the human heart that have been examined by artificial intelligence. This analysis is based on images taken via Cardiac Computer Tomography Angiogram (CTA), a new, but rapidly growing manner of scanning for plaques.
“We focus on the entire heart, so every artery, and its branches, and then atherosclerosis characterization and quantification,” says Min. “We look at all of the plaque buildup in the artery, [and] the walls of the artery, which historical and traditional methods that we’ve used in cardiology have never been able to do.”
Cleerly is a web application, and it requires that a CTA image specifically, which the A.I. is trained to analyze, is actually taken when patients go in for a checkup.
When a patient goes in for a heart exam after experiencing a symptom like chest pain, there are a few ways they can be screened. They might undergo a stress test, an echocardiogram (ECG), or a coronary angiogram – a catheter and x-ray-based test. CTA is a newer form of imaging in which a scanner takes detailed images of the heart, which is illuminated with an injected dye.
Cleerly’s platform is designed to analyze those CTA images in detail, but they’ve only recently become a first-line test (a go-to, in essence) when patients come in with suspected heart problems. The European Society of Cardiology updated guidelines to make CTA a first-line test in evaluating patients with chronic coronary disease. In the UK, it became a first-line test in the evaluation of patients with chest pain in 2016.
CTA is already used in the US, but guidelines may expand how often it’s actually used. A review on CTA published on the American College of Cardiology website notes that it shows “extraordinary potential.”
There’s movement on the insurance side, too. In 2020, United Healthcare announced the company will now reimburse for CTA scans when they’re ordered to examine low-to medium risk patients with chest pain. Reimbursement qualification is obviously a huge boon to broader adoption.
CTA imaging might not be great for people who already have stents in their hearts, or, says Min, those who are just in for a routine checkup (there is low-dose radiation associated with a CTA scan). Rather, Cleerly will focus on patients who have shown symptoms or are already at high risk for heart disease.
The CDC estimates that currently 18.2 million adults currently have coronary artery heart disease (the most common kind), and that 47 percent of Americans have one of the three most prominent risk factors for the disease: high blood pressure, high cholesterol, or a smoking habit.
These shifts (and anticipated shifts) in guidelines suggest that a lot more of these high-risk patients may be getting CTA scans in the future, and Cleerly has been working on mining additional information from them in several large-scale clinical trials.
There are plenty of different risk factors that contribute to heart disease, but the most basic understanding is that heart attacks happen when plaques build up in the arteries, which narrows the arteries and constricts the flow of blood. Clinical trials have suggested that the types of plaques inside the body may contain information about how risky certain blockages are compared to others beyond just much of the artery they block.
A trial on 25,251 patients found that, indeed, the percentage of construction in the arteries increases the risk of heart attack. But the type of plaque in those arteries identified high-risk patients better than other measures. Patients who went on to have sudden heart attacks, for example, tended to have higher levels of fibrofatty or necrotic core plaque in their hearts.
These results do suggest that it’s worth knowing a bit more detail about plaque in the heart. Note that Min is an author of this study, but it was also conducted at 13 different medical centers.
As with all A.I based diagnostic tools the big question is: How well does it actually recognize features within a scan?
At the moment FDA documents emphasize that it is not meant to supplant a trained medical professional who can interpret the results of a scan. But tests have suggested it fares pretty well.
A June 2021 study compared Cleerly’s A.I analysis of CTA scans to that of three expert readers, and found that the A.I had a diagnostic accuracy of about 99.7 percent when evaluating patients who had severe narrowing in their arteries. Three of nine study authors hold equity in Cleerly.
With this most recent round of funding, Min says he aims to pursue more commercial partnerships and scale up to meet the existing demand. “We have sort of stayed under the radar, but we came above the radar because now I think we’re prepared to fulfill demand,” he says.
Still, the product itself will continue to be tested and refined. Cleerly is in the midst of seven performance indication studies that will evaluate just how well the software can spot the litany of plaques that can build up in the heart.
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