Month: February 2021

With tech giants pouring billions of dollars into artificial intelligence projects, it’s hard to see how startups can find their place and create successful business models that leverage AI. However, while fiercely competitive, the AI space is also constantly causing fundamental shifts in many sectors. And this creates the perfect environment for fast-thinking and -moving startups to carve a niche for themselves before the big players move in.

Last week, technology analysis firm CB Insights published an update on the status of its list of top 100 AI startups of 2020 (in case you don’t know, CB Insight publishes a list of 100 most promising AI startups every year). Out of the hundred startups, four have made exits, with three going public and one being acquired by Facebook.

A closer look at these startups provides some good hints at what it takes to create a successful business that makes use of AI. And (un)surprisingly, artificial intelligence is a small part — albeit an important one — of a successful product management strategy. Here are some of the key takeaways from AI startups that have managed to reach a stable status.

Lemonade: AI complements a successful product strategy

Lemonade Inc. CEO Daniel Schrieber at TC Disrupt 2018

Lemonade, an insurtech startup founded in 2015, made its initial public offering in July with a $1.7 billion valuation. Lemonade is an online platform that aims to address some of the key problems of the traditional home insurance industry. The company has been able to develop its business through smart design and a good marketing strategy. The AI component was built on top of that.

The company’s website and mobile app are very easy to use. The process of buying insurance and filing claims with the app and website goes through digital assistants and is much faster than traditional insurance companies. As one of the first movers in the insurtech space, Lemonade had the edge over other similar companies that have cropped up in recent years, and it was able to quickly snatch a lot of users who were looking for a shift from traditional insurance model to one that was more tech-focused.

[Read: How much does it cost to buy, own, and run an EV? It’s not as much as you think]

Lemonade’s business model and messaging are also interesting. The company takes a flat fee from premiums, which means the company doesn’t make a profit from denying claims. The unclaimed money goes to charities of users’ choice. The company also says that it will not invest premiums into heavily polluting industries and companies that cause harm. So, basically, Lemonade is marketing itself as the good guy in a historically reviled industry, on a mission, per the company’s words, to “transform insurance from a necessary evil to a social good.”

Insurance depends a lot on data, and established agencies have more than a century of data they can use to develop risk models and create insurance policies. Lemonade didn’t have the data of traditional agencies, but it also didn’t have their baggage of customers and old policies. It was able to create its entire technology stack from the ground up to cater to the needs of an AI factory.

With the entire experience being digitized, the company can collect a lot more data from each customer interaction, including data points that other agencies do not capture. This enables the company to create machine learning models that not only predict insurance risk with growing accuracy over time but can also create automation and personalization opportunities that were impossible before. The company has two AI chatbots: Maya helps you create your insurance plan in a few minutes, and Jim handles the claims process. According to the company, AI handles a third of the cases and pays claims in a matter of minutes. The rest of the claims are transferred to human agents. The chatbot continues to improve as it gathers more data.

The company believes that with time, the AI will give it the edge over traditional agencies and allow it to provide much more affordable plans to customers. And its $480 million pre-IPO funding and its post-IPO growth show that investors believe its plan can work.

https://www.youtube.com/watch?v=A2zn6nEm0gQ

Lemonade’s head start is its biggest protection. Other startups that would want to copy its business model don’t have its data and can’t create equally efficient AI models. And it has also created a protective moat against traditional insurance agencies, which are much slower to move into new areas. By the time they do create their own AI factories, Lemonade will have carved a comfortable niche for itself.

Butterfly Network: Specialized hardware with AI enhancements

Butterfly Network iQ ultrasound probe

Butterfly Network will be listed on the New York Stock exchange after a $1.5 billion special purpose acquisition company (SPAC) merger with Longview Capital later this year.

The company’s product is Butterfly iQ, a medically approved single-probe, whole-body ultrasound device that connects to a smartphone and works with an accompanying mobile app. The device costs $2,000, which is much more affordable than the five- and six-digit-priced ultrasound sets usually found at hospitals. The company aims to make high-quality ultrasound imaging available to communities that can’t afford high-end devices and bring portable scanning to places where the bulky ultrasound sets can’t go.

iQ also uses artificial intelligence to create use cases that are not available on other ultrasound devices. For instance, one of the AI features of iQ is a slider in the app that shows the quality of the image to the user. As the user moves the probe on the patient’s body, the slider shifts to show whether the device is getting a good capture or not. The feature uses an artificial neural network that has been trained on tens of thousands of images to discriminate between good and bad images. For instance, frontline responders or clinics whose staff don’t have the expertise with ultrasound can use the device to get proper images and send them to experts for further analysis.

The device and app come bundled with a bunch of cloud storage and sharing features that facilitate the use of data in a broader health care context.

The company is also working to add new machine learning-powered features to help with measurement and analysis.

So here too, I think that AI is a small but important part of the overall business. The biggest value comes from the hardware. The small, portable ultrasound device allows Butterfly to differentiate itself from other manufacturers and create value for untapped segments of the market. AI is the added value that helps it improve the software stack that builds on top of the hardware. Given that the device uses consumer smartphones, it also has the potential to add new AI features and continually improve its product’s performance as mobile device hardware becomes better.

The one risk I see in Butterfly’s AI business is the possibility of similar moves from household names such as Philips and Siemens. Should health tech giants decide to enter the handheld ultrasound business, Butterfly Network will need to find something that can protect its products against copycats. One possible solution would be for Butterfly to work out a privacy-friendly plan to collect ultrasound data from iQ devices to improve the performance of its AI models. But it will not be very easy, given the sensitive nature of health data.

C3.ai: Enterprise AI can work if you have the reputation

C3.ai website

C3.ai, another one of the successful AI startups mentioned by CB Insights, is a provider of enterprise AI software. C3.ai’s pre-IPO valuation was $4 billion, but on the first day of trading, its market cap skyrocketed above $13 billion.

C3.ai software helps companies build AI models on top of their data for predictive maintenance, improved inventory management, fraud detection, energy management, and other operational enhancements that can reduce costs and increase productivity. C3.ai is not a provider of cloud services but its software is compatible with most top cloud providers such as Microsoft Azure, Amazon Web Services, Google Cloud, and IBM Cloud.

Under normal circumstances, C3.ai’s product strategy would be considered risky. From a technical standpoint, it has no key differentiator. It is providing services that can easily be replicated by another company that has the right resources, including the very cloud services its software integrates with. And since its founding in 2009, the company has changed its name twice from C3 Energy to C3 IoT and then to C3.ai, which sounds a bit opportunistic.

What makes C3.ai different, however, is its founder Thomas Siebel, a billionaire and a well-known and respected entrepreneur. C3.ai’s success hinges not on a lot of small customers but on creating ripple effects in different sectors by acquiring big customers. In this respect, having a person on board who has the reputation and experience of Siebel can make a big difference. Currently, C3.ai’s customers include machinery manufacturer Caterpillar, oil and gas services company Baker Hughes, and energy company Engie, all big names in their respective industries. Interestingly, 36 percent of its revenue in 2020 came from Baker Hughes and Engie.

Therefore, although C3.ai provides very good AI development tools, the company’s success can be largely attributed not to its unique AI capabilities but its customer acquisition and retention strategy. I’m not sure if that would have been possible without having someone at the helm of the company who has strong connections in different markets and a reputation for delivering great products.

Mapillary: The value of data

Mapillary CEO Jan Erik Solem at RAAIS 2017

The final company that’s worth examining in the CB Insights list is Mapillary, acquired by Facebook in June for an undisclosed amount. Mapillary launched in 2013 to create a massive dataset of street-level images, rivaling Google’s Street View service.

Since its founding, Mapillary has collected more than one billion high-resolution images from cities around the world. Before being acquired by Facebook, Mapillary had partnered with Amazon’s AI platform to extract information from images through computer vision.

Mapillary didn’t have a super-advanced AI application or a very promising roadmap to making a profit over its data. But its data and services could prove to be a great addition to a larger ecosystem of AI software, such as that of Facebook. There are many ways Facebook, which is in the business of knowing more and more about its users, can turn a profit from Mapillary’s data. For now, we know that it will be integrating Mapillary’s data and applications into Facebook’s augmented reality and Marketplace platforms. And there are many other uses Facebook’s AI research unit can have for exclusive access to this large data set of labeled street images.

Therefore, I don’t quite see Mapillary as an AI success story, but its acquisition highlights the value of data in the AI industry. Large tech companies are often in search of ways to obtain exclusive data to hone their AI models and gain an edge over competitors. And they’re more than willing to take a shortcut by purchasing another company’s data—and perhaps the whole company with it.

The “AI startup” misnomer

I think “AI startup” is a misnomer when applied to many of the companies included in the CB Insights list because it puts too much focus on the AI side and too little on the other crucial aspects of the company.

Successful companies start by addressing an overlooked or poorly solved problem with a sound product strategy. This gives them the minimum market penetration needed to establish their business model and gather data to gain insights, steer their product in the right direction, and train machine learning models. Finally, they use AI as a differentiating factor to solidify their position and maintain the edge over competitors.

No matter how advanced, AI algorithms alone don’t make a successful startup nor a business strategy.

This article was originally published by Ben Dickson on TechTalks, a publication that examines trends in technology, how they affect the way we live and do business, and the problems they solve. But we also discuss the evil side of technology, the darker implications of new tech and what we need to look out for. You can read the original article here. 

Published February 8, 2021 — 08:42 UTC

This article was originally published by Christopher Carey on Cities Today, the leading urban mobility and innovation news platform reaching an international audience of city guides. For the latest updates, follow Cities Today on Twitter, Facebook, LinkedIn, Instagram and YouTube or sign up for Cities Today News. The UK government has pledged £ 20 million ($ 27.4 million) to help local authorities install 4,000 electric chargers on the road over the next two years. Funding through the On-Street Residential Chargepoint Scheme will double the number of government-sponsored electric chargers to nearly 8,000. Since the program started in 2017, more than 140 local …

That story continues on the Next Web

A team of researchers from University College Maastricht recently published a study examining the use of GPT-3 as an email manager. As someone with an inbox that can only be described as ridiculous, I am intrigued.

Can GPT-3 help me?

The big idea: We spend hours a day reading and answering emails. What if an AI could automate both processes?

The Maastricht team explored the idea of ​​letting go of GPT-3 in our email systems from a pragmatic point of view. Rather than focusing on exactly how well GPT-3 can respond to certain emails, the team assessed whether it would make sense to try at all.

Her article (read here) breaks down the potential effectiveness of GPT-3 as an email secretary by examining how useful it is compared to finely tuned machines, how financially viable it is compared to human workers and how effective machine-generated errors could be, sender and recipient.

Background: The quest for a better email client is endless, but ultimately it’s all about getting GPT-3 to respond about incoming emails. According to the researchers:

Our research shows that there is a market for GPT-3-based email rationalization across several economic sectors, only a few of which we will examine. In all sectors, the harm of a small wording error appears to be minor as the content generally does not involve large amounts of money or human security.

The authors describe use cases in the fields of insurance, energy and public administration.

Objection: First of all, it should be pointed out that this is pre-printed paper. Often times this means that the science is good, but the paper itself is still under revision. This particular paper is a little messy right now. For example, three separate sections contain the same information, making it difficult to really get the point of study.

It seems to suggest that it would save us time and money if GPT-3 could be applied to the task of replying to our business emails. But that’s a gigantic “if”.

GPT-3 lives in a black box. A person would have to proofread every email they send, as there is no way they can ever be certain that they are not saying anything that will encourage litigation. Aside from fears that the machine might generate offensive or incorrect text, there is also the problem of figuring out how good a bot with general knowledge would be at the job.

GPT-3 was trained on the internet so it might be able to tell you the wingspan of an albatross or the 1967 World Series winner, but it certainly can’t make up its mind whether to redeem a birthday card for a co-worker or if you’re interested are to chair a new subcommittee.

The point is, GPT-3 is likely to be less responsive to general emails than a simple chatbot trained to pick a pre-generated response.

Take quickly: A little googling tells me that the landline phone wasn’t ubiquitous in the US until 1998. And now, just a few decades later, only a tiny fraction of US households still have landlines.

I wonder if email will be the standard for communication for much longer – especially if the last line of innovation is finding ways to keep us out of our own inboxes. Who knows how long we could be from a hypothetical version of OpenAIs GPT that is trustworthy enough to be worth using on any commercial level.

The research here is commendable and the paper makes interesting reading, but ultimately, the usefulness of GPT-3 as an email responder is purely academic. There are better solutions for inbox filtering and automated response than a brute force text generator.

Published on February 8, 2021 – 20:17 UTC

File this at: While the rest of the world was busy getting virtual assistants to tell dad jokes, researchers in Estonia figured out how to falsify people at the molecular level.

We have seen a delightful onslaught of AI-generated content from Generative Adversary Networks (GANs) in the past few years trying to create so-called “deepfake” images.

There is that person doesn’t exist. And that cat doesn’t exist. You can even generate feet, resumes, and waifu that aren’t there. It’s amazing how realistic some of the images created from the air by the AI ​​can be.

However, this is the first time an AI has generated the recipe for a viable, unique human by creating synthetic genomes.

A team of researchers from Estonia has developed a machine learning system that can be used to generate unique genome sequences. These computer-generated counterfeits could play an important role in the future of DNA research.

According to the team’s research report:

Generative neural networks have been used effectively in many different fields over the past decade, including machine-dreamed photo-realistic images. In our work we apply a similar concept to genetic data to automatically learn its structure and produce, for the first time, realistic genomes of high quality.

These AI sequences, called “artificial genomes”, are indistinguishable from actual human genomes, except that they are entirely synthetic. This means researchers do not have to deal with ethical privacy concerns.

According to the current research paradigm, researchers need to protect DNA to ensure the privacy of the people it belongs to. This comes with added accuracy and, in many cases, a drought in available data as facilities cannot share their data sets. Synthetic genomes should go a long way in solving these problems.

Take quickly: This is fantastic news for medical researchers, and a clear case of GAN technology being used forever. However, this work sheds light on some ethical problems of the near and distant future that we will one day have to face.

In the near future, it will be easier for bad actors to create fake personas that can withstand even the strictest of inspections. Not that we imagine a scenario in which a fraudster has to produce a fake copy of his genome, but it is in the unknown unknowns that vulnerabilities grow fastest.

In the long term … if Skynet had this GAN, it wouldn’t have had to make so many machines that look like Arnold Schwarznegger. Someday this technology could reach a point where the philosophical question “Am I a robot?” Becomes a very valid question for any person.

Read the whole paper here.

Published on February 8, 2021 – 22:07 UTC