By day, Alain Paillou is the head of water quality for the Bourgogne region of France. But when the stars come out, he indulges his other passions.
Paillou takes exquisitely crisp pictures of the moon, stars and planets — a hobby that combines his lifelong love of astronomy and technology.
Earlier this year, he chronicled on an NVIDIA forum his work building what he calls SkyNano, a GPU-powered camera to take detailed images of the night sky using NVIDIA’s Jetson Nano.
“I’ve been interested in astronomy from about eight or 10 years old, but I had to quit my studies for more than 30 years because of my job as an aerospace software engineer,” said Paillou in an interview from his home in Dijon.
Paillou went back to school in his early 30s to get a degree and eventually a job as a hydrogeologist. “I came back to astronomy after my career change 20 years ago when I lived in Paris, where I started taking photographs of the moon, Jupiter and Saturn,” he said.
“I really love technology and astronomy needs technical competence,” he said. “It lets me return to some of the skills of my first job — developing software to get the best results from my equipment — and it’s very interesting to me.”
Seeing Minerals on the Moon
Paillou loves to take color-enhanced pictures of the moon that show the diversity of its blue titanium and orange iron-oxide minerals. And he delights in capturing star-rich pictures of the night sky. Both require significant real-time filters, best run on a GPU.
Around his Dijon home, as in many places, “the sky is really bad with light pollution from cities that make images blurry,” he said. “I can see 10-12 stars with my eyes, but with my system I can see thousands of stars,” he said.
“If you want to retrieve something beautiful, you need to apply real-time filtering with an A/V compensation system. I built my own system because I could not find anything I could buy that matched what I wanted,” Paillou said.
Building the SkyNano
His first prototype mounted a ZWO ASI178MC camera using a Sony IMX178 color sensor on a platform with a gyro/compass and a two-axis mount controlled by stepper motors. Initially he used a Raspberry Pi 3 B+ to run Python programs that controlled the mount and camera.
The board lacked the muscle to drive the real-time filters. After some more experiments, he asked NVIDIA for help in his first post on the Jetson Nano community projects forum on June 21. By July 5, he had a Jetson Nano in hand and started loading OpenCV filters on it using Python.
By the end of July, he had taught himself PyCUDA and posted significant results with it. He released his routines on GitHub and reported he was ready to start taking pictures.
On Aug. 2, he posted his camera’s first digitally enhanced picture of the Copernicus crater on the moon as well as a YouTube video showing a Jetson Nano-enhanced night sky. By October, he posted stunning color-enhanced pictures of the moon (see above), impressive night-vision capabilities and a feature for tracking satellites.
Paillou’s project became the most popular thread on the NVIDIA Jetson Project’s forum with more than 3,100 views to date. Along the way, he gave a handful of others tips for their own AI projects, many of which are available here.
Exploring Horizons in Space and Software
“Twenty years ago, computers were not powerful enough to do this work, but today a little computer like the Jetson Nano makes it really interesting and it’s not expensive,” said Paillou, whose laptop connected to the system also uses an NVIDIA GPU.
Like “innovation,” machine learning and artificial intelligence are commonplace terms that provide very little context for what they actually signify. AI/ML spans dozens of different fields of research, covering all kinds of different problems and alternative and often incompatible ways to solve them.
One robust area of research here that has antecedents going back to the mid-20th century is what is known as stochastic optimization — decision-making under uncertainty where an entity wants to optimize for a particular objective. A classic problem is how to optimize an airline’s schedule to maximize profit. Airlines need to commit to schedules months in advance without knowing what the weather will be like or what the specific demand for a route will be (or, whether a pandemic will wipe out travel demand entirely). It’s a vibrant field, and these days, basically runs most of modern life.
Warren B. Powell has been exploring this problem for decades as a researcher at Princeton, where he has operated the Castle Lab. He has researched how to bring disparate areas of stochastic optimization together under one framework that he has dubbed “sequential decision analytics” to optimize problems where each decision in a series places constraints on future decisions. Such problems are common in areas like logistics, scheduling and other key areas of business.
The Castle Lab has long had industry partners, and it has raised tens of millions of dollars in grants from industry over its history. But after decades of research, Powell teamed up with his son, Daniel Powell, to spin out his collective body of research and productize it into a startup called Optimal Dynamics. Father Powell has now retired full-time from Princeton to become Chief Analytics Officer, while son Powell became CEO.
The company raised $18.4 million in new funding last week from Bessemer led by Mike Droesch, who recently was promoted to partner earlier this year with the firm’s newest $3.3 billion fundraise. The company now has 25 employees and is centered in New York City.
So what does Optimal Dynamics actually do? CEO Powell said that it’s been a long road since the company’s founding in mid-2017 when it first raised a $450,000 pre-seed round. We were “drunkenly walking in finding product-market fit,” Powell said. This is “not an easy technology to get right.”
What the company ultimately zoomed in on was the trucking industry, which has precisely the kind of sequential decision-making that father Powell had been working on his entire career. “Within truckload, you have a whole series of uncertain variables,” CEO Powell described. “We are the first company that can learn and plan for an uncertain future.”
There’s been a lot of investment in logistics and trucking from VCs in recent years as more and more investors see the potential to completely disrupt the massive and fragmented market. Yet, rather than building a whole new trucking marketplace or approaching it as a vertically-integrated solution, Optimal Dynamics decided to go with the much simpler enterprise SaaS route to offer better optimization to existing companies.
One early customer, which owned 120 power units, saved $4 million using the company’s software, according to Powell. That was a result of better utilization of equipment and more efficient operations. They “sold off about 20 vehicles that they didn’t need anymore due to the underlying efficiency,” he said. In addition, the company was able to replace a team of ten who used to manage trucking logistics down to one, and “they are just managing exceptions” to the normal course of business. As an example of an exception, Powell said that “a guy drove half way and then decided he wanted to quit,” leaving a load stranded. “Trying to train a computer on weird edge events [like that] is hard,” he said.
Better efficiency for equipment usage and then saving money on employee costs by automating their work are the two main ways Optimal Dynamics saves money for customers. Powell says most of the savings come in the former rather than the latter, since utilization is often where the most impact can be felt.
On the technical front, the key improvement the company has devised is how to rapidly solve the ultra-complex optimization problems that logistics companies face. The company does that through value function approximation, which is a field of study where instead of actually computing the full range of stochastic optimization solutions, the program approximates the outcomes of decisions to reduce compute time. We “take in this extraordinary amount of detail while handling it in a computationally efficient way,” Powell said. That’s where we have really “wedged ourselves as a company.”
Early signs of success with customers led to a $4 million seed round led by Homan Yuen of Fusion Fund, which invests in technically-sophisticated startups (i.e. the kind of startups that take decades of optimization research at Princeton to get going). Powell said that raising the round was tough, transpiring during the first weeks of the pandemic last year. One corporate fund pulled out at the last minute, and it was “chaos ensuing with everyone,” he said. This Series A process meanwhile was the opposite. “This round was totally different — closed it in 17 days from round kickoff to closure,” he said.
With new capital in the bank, the company is looking to expand from 25 employees to 75 this year, who will be trickling back to the company’s office in the Flatiron neighborhood of Manhattan in the coming months. Optimal Dynamics targets customers with 75 trucks or more, either fleets for rent or private fleets owned by companies like Walmart who handle their own logistics.
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(Reuters) — IBM said on Tuesday it would buy Waeg, a consulting partner for Salesforce, in a deal that will extend its range of services and support its hybrid cloud and artificial intelligence strategy.
The deal to acquire Waeg, which is based in Brussels and serves clients across Europe, complements IBM’s acquisition in January of 7Summits, a U.S. consultancy that specialises in Salesforce’s customer management software.
“Waeg’s strength in Salesforce consulting services will be key to creating intelligent workflows that allow our clients to keep pace with changing customer and employee needs and expectations,” Mark Foster, senior vice president of IBM Services and Global Business Services, said.
Waeg employs a team of 130 ‘Waegers’ in locations that include Belgium, Denmark, France, Ireland, Poland, Portugal and the Netherlands.
The terms were not disclosed for the deal, which is subject to customary closing conditions and is expected to be completed this quarter.
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Sylvain Kalache is the co-founder of Holberton, an edtech company training digital talent in more than 10 countries. An entrepreneur and software engineer, he has worked in the tech industry for more than a decade. Part of the team that led SlideShare to be acquired by LinkedIn, he has written for CIO and VentureBeat.
AI is driving the paradigm shift that is the software industry’s transition to data-centric programming from writing logical statements. Data is now oxygen. The more training data a company gathers, the brighter will its AI-powered products burn.
Why is Tesla so far ahead with advanced driver assistance systems (ADAS)? Because no one else has collected as much information — it has data on more than ten billion driven miles, helping it pull ahead of competition like Waymo, which has only about 20 million miles. But any company that is considering using machine learning (ML) cannot overlook one technical choice: supervised or unsupervised learning.
There is a fundamental difference between the two. For unsupervised learning, the process is fairly straightforward: The acquired data is directly fed to the models, and if all goes well, it will identify patterns.
Elon Musk compares unsupervised learning to the human brain, which gets raw data from the six senses and makes sense of it. He recently shared that making unsupervised learning work for ADAS is a major challenge that hasn’t been solved yet.
A major part of real-world AI has to be solved to make unsupervised, generalized full self-driving work, as the entire road system is designed for biological neural nets with optical imagers
Supervised learning is currently the most practical approach for most ML challenges. O’Reilly’s 2021 report on AI Adoption in the Enterprise found that 82% of surveyed companies use supervised learning, while only 58% use unsupervised learning. Gartner predicts that through 2022, supervised learning will remain favored by enterprises, arguing that “most of the current economic value gained from ML is based on supervised learning use cases”.
Short introduction: I’m Jerry Udensi, CTO of a Nigerian-Malaysian tech company: Lyshnia Limited. Prior to working full time with Lyshnia (a company I founded in 2013 with my elder brother), I worked in the AI industry in Malaysia and Singapore. I have built Natural Language AI systems for large corporations such as Allianz SE, and Insurance Technology for companies like Malaysia’s Insuradar Sdn.
The reason for my short introduction is to show you my background in building AI powered systems. Natural Language Processing is a field I’ve actively been in for over 3 years now so you’d think building a Transactional Chat Bot that sells only 10 products shouldn’t be an issue for me right? Well you’d be right if the customers were people who read.
In the paragraphs to follow, I will highlight what I’ve learnt building and maintaining Jane B(Just another Non-Existent Bot) which attends to approx. 1000 customers every day.
There’s this old saying that goes “if you want to hide something from a Black Man, put it in a book”. Unfortunately, this is the case with over 70% of the Customers who used the bot.
When you first message the bot, it greets you, let’s you know that you’re chatting with a Bot, then gives you 4 options to choose from.
5 out of 10 people ignore the initial message and go ahead to write what they want, 2 out of 10 people would read but not understand and therefore reply confusedly like in the image below:
For the 5 who initially ignored the Menu message, we automatically resend the message, and 4 out of 5 would go on to reply appropriately, while 1 of 5 would complain of how stressful the process is and probably never chat again.
Yes, we get it. You live in France, but do you want it Delivered or will you Pick it up? (some customers send people in to do a pick up for them)
Jane has been simplified to understand even incorrect English, and giving the customers hints on how to reply, yet a lot of those who chat her simply ignore instructions, and rather type a thousand words than one that Jane would understand.
You would think it’ll be easier and less stressful for customers to simply reply “1” rather than type out “I want to make an order”, but no. Chat after chat, you will realise a lot of people are saying unnecessary things before or after their actual intention. For Chat Bot providers, this can be a nightmare because the Chat Bot asked a question and is listening for a Natural Language answer which is very hard to predict if the users response is in line with your desired answer.
Even for a human, it is hard to understand another humans intentions when spoken out of context
For the Chat above, the Bot was asking the user to confirm the items she wants to buy, but the user instead replies saying where they live. Totally out of context.
Getting instant replies is a drug people are addicted to. Customers are told that this is a chat bot which only takes orders and track orders, then given another number to chat for consultancy to speak to a human. Yet, they keep coming back just minutes later to complain to the Bot that they’re not getting responses there.
Something else I noticed while analysing the chat response times is that the Customers get so hooked on the instant replies that if at any point, the chat bot delays their response for even just 1 minute they start asking why they’re not getting any response.
On the good side, customer who read and follow the short and simple instructions are able to place their orders in less than 2 minutes from a platform their comfortable with (WhatsApp) while feeling like they’re chatting with a human.