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What Would We Do If an Asteroid Hit Earth? NASA Is Simulating It This Week

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About a month ago, an asteroid that was estimated to be at least a quarter-mile wide zoomed past Earth at a speed of 77,000 miles per hour. Though it was 1.25 million miles away even at its closest, the asteroid (called, unimaginatively, Object 2001 FO32) was considered “close” to Earth; in the grand scheme of the entire galaxy and beyond, 1.25 million miles isn’t much.

Though space is replete with asteroids of all sizes flying about, it seems the risk of one of them hitting Earth anytime in the near future is slim to nonexistent. NASA, however, isn’t taking any chances. This week at the 7th IAA Planetary Defense Conference, the space agency’s Jet Propulsion Laboratory’s Center for Near Earth Object Studies (CNEOS) is leading a simulated asteroid impact scenario—in other words, pretending an asteroid is on its way to hitting Earth.

Why, you ask? To make sure we’re ready if it happens for real.

Since there’s not a way to physically simulate an asteroid hitting Earth (well, other than launching some kind of huge object into space in such a way that it comes flying back at us at warp-speed, which doesn’t sound worth trying even for whatever lessons we’d learn from it), the simulation is about practicing coordination between various US federal agencies, international space agencies, and other decision-makers, scientific institutions, and emergency managers.

Throughout this week, participants in the simulation are being briefed with consistently-evolving information about the fictitious asteroid—how fast it’s moving, how big it is, the trajectory it’s taking through space, where on Earth it’s likely to hit, etc. Participants then have to work together to make decisions and give feedback for next steps. In real life, there’s an international agreement that a 1 in 100 chance of impact is the threshold to begin response actions.

“Each time we participate in an exercise of this nature, we learn more about who the key players are in a disaster event, and who needs to know what information, and when,” said Lindley Johnson, NASA’s Planetary Defense Officer. “These exercises ultimately help the planetary defense community communicate with each other and with our governments to ensure we are all coordinated should a potential impact threat be identified in the future.”

This isn’t the first time this sort of simulation has taken place; the exercise is part of the National Near-Earth Object Preparedness Strategy and Action Plan that the White House published in 2018, and NASA has participated in several other impact scenarios, some in conjunction with the Federal Emergency Management Agency (FEMA).

Also, the simulation is about to get a little more real, with an actual attempt to deflect an asteroid in space initiating this summer. If that sounds very Armageddon-like to you, you’re not terribly far off; NASA’s Double Asteroid Redirection Test (DART) mission is scheduled to launch in July or later on one of SpaceX’s Falcon 9 rockets, heading for an asteroid called Didymos. The goal is to crash into the asteroid’s secondary body, a “moonlet” called Dimorphos that’s as wide as one and a half football fields, and change the speed of its orbit around the main body.

NASA DART asteroid mission
Artist’s concept of the Double Asteroid Redirection Test (DART) mission. Image Credit: NASA/Johns Hopkins University Applied Physics Laboratory

“DART will be the first test for planetary defense, and the data returned after it impacts Dimorphos will help scientists better understand one way we might mitigate a potentially hazardous Near Earth Object discovered in the future,” said Andrea Riley, program executive for DART at NASA Headquarters. “While the asteroid DART impacts poses no threat to Earth, it is in a perfect location for us to perform this test of the technology before it may actually be needed.”

Here’s hoping it won’t actually be needed anytime soon, or ever. In any case, there’s nothing wrong with hoping for the best but preparing for the worst.

Let’s say Object 2001 FO32 had hit Earth. It was, relatively speaking, pretty small, but at a speed of 77,000 miles per hour, it would have done some serious, serious damage. In the wake of the Covid-19 pandemic, it seems not just smart, but essential to prepare for crises that would require cooperation on a global scale. Many of the issues and differences nations squabble over would quickly become meaningless—and remind us of our shared humanity and shared home—if we were faced with this sort of threat. Given that the handling of the pandemic left much to be desired in terms of global cooperation, maybe we need more reminders of this, and more practice at doing it right.

Image Credit: NASA

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Source: https://singularityhub.com/2021/04/28/what-would-we-do-if-an-asteroid-hit-earth-nasa-is-simulating-it-this-week/

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Father and son duo take on global logistics with Optimal Dynamics’ sequential decision AI platform

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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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Source: https://techcrunch.com/2021/05/18/optimal-dynamics-series-a/

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IBM will buy Salesforce partner Waeg to boost hybrid cloud, AI strategy

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Elevate your enterprise data technology and strategy at Transform 2021.


(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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Source: https://venturebeat.com/2021/05/18/ibm-will-buy-salesforce-partner-waeg-to-boost-hybrid-cloud-ai-strategy/

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For companies that use ML, labeled data is the key differentiator

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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.

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”.

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Source: https://techcrunch.com/2021/05/18/for-companies-that-use-ml-labeled-data-is-the-key-differentiator/

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My Experience Building a WhatsApp Chat Bot for a Nigerian Company

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Jerry Udensi
Summary.

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.

The first 3 messages you receive after chatting the first time.

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:

A customers reply

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.

1. Chatbot Trends Report 2021

2. 4 DO’s and 3 DON’Ts for Training a Chatbot NLP Model

3. Concierge Bot: Handle Multiple Chatbots from One Chat Screen

4. An expert system: Conversational AI Vs Chatbots

Why? 🤦🏽‍♂️

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.

Ok

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.

We as Humans need to do better. Thank you.

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Source: https://chatbotslife.com/my-experience-building-a-whatsapp-chat-bot-for-a-nigerian-company-b19c02c7d68?source=rss—-a49517e4c30b—4

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