Most exciting series B AI startups

Most exciting series B AI startups

If you want to see the full list of AI companies, we have 600+ enterprise AI companies already categorized. You can reach the full list here. Wanted to provide the source data before I play the pundit.

As for punditry, I’ll try to be data driven. First, I exclude B2C AI companies as we don’t track them yet. Then, I assume you are interested in companies that raised less than $10M, as a pre-seed+seed+series A can accumulate to $10M.

Let’s also define awesome! To me, an awesome company needs to punch above its weight. Ideally, that means more revenue than expected but revenue is a hard to get metric for startups. So I picked companies that had more customer references and buzz compared to companies launched at similar times and raised similar amounts. To do this quickly, I examined a few of the popular AI use cases like predictive maintenance but the list can get a bit more interesting when I have a bit more time to play the pundit and include other AI use cases as well. Read more

Discover the right AI vendors for your business for free in 30 seconds

Discover the right AI vendors for your business for free in 30 seconds

Here’s the 30 second solution: Choose the use case where you need a solution, give us your email and company name and we will get back to you with 48 hours with a list of relevant vendors. Here’s the form you need.

Current vendor selection process is broken

Now that you know the solution, let me try to convince you why it makes sense: Vendor selection is broken. And we start fixing it from the beginning: Choosing a shortlist of vendors.

To get your home remodeled, you don’t need to spend hours searching reviews and specs. You would go to thumbtack where you can find vendors, their references and reviews. You can get quotes on the platform and deal with all aspects of buying the service. Btw, I am sure some people still choose the difficult path and do the research themselves, but the more efficient method is definitely to go with thumbtack. Let’s compare that with the B2B experience. Read more

How do Neural Networks work?

How do Neural Networks work?

Neural networks are complex but as much exciting for many reasons. They also motivate us to understand our own cognitive mechanism better, then reflect it to machines. We already have amazing examples of deep neural networks such as Google DeepMind’s AlphaGo which beat Lee Sedol, winner of 18 world titles and widely considered to be the greatest player of the past decade.

Image classification, natural language processing and computerized axial tomography classification are some of the areas where neural networks are used. Neural networks are smart in their specific domains but lack generalization capabilities. Their intelligence needs adjustments. Read more

Autonomous trucks will destroy >3M jobs in 15 years

Autonomous trucks will destroy >3M jobs in 15 years

Movie Logan takes place in 2029. Most things seem similar to today except trucks. They are autonomous. Will Hollywood get this right? Is 10-15 years a realistic time frame for autonomous vehicles?

What exactly is autonomous driving?

First, let’s agree on what we are discussing. To understand in detail, let’s look at levels of autonomous driving defined by Society of Automobile Engineers:

L0 No automation: with human in charge potentially augmented by automated warning and intervention systems

L1 Driver assistance: Driving mode-specific driver assistance system of either steering or acceleration/deceleration. Example: Cruise control Read more

When should you build your own AI solution?

When should you build your own AI solution?

We previously explained why most small and non-tech companies should stick to working with AI vendors than building their own solutions. As with any generalization, there are exceptions.

If the solution passes all these tests then you absolutely need to build your own AI solutions:

  • You have access to a large amount of unique proprietary data. Any large B2C company has significant data and if this data exists in multiple companies, it is likely that AI vendors probably already worked with the data and have the experience to mine it effectively. However, if this data does not exist anywhere else in the market, then vendors will not have experience with the data.
  • Minor improvements in processing this data can lead to significant financial impact. Here the word minor is important. It is easy to work with a vendor who can quickly build a solution that performs OK. However, if minor improvements are impactful, then you want a focused team that has complete alignment of incentives with your business. It is easier to achieve that level of focus and alignment with an in-house solution.
  • You already have access to or can easily access AI talent. This is probably the hardest part. An engineer willing to experiment with AI and an AI expert are two very different things. Experience helps in fine-tuning models and working with large datasets and an experienced team can provide better results faster.

Discover alternatives to in-house solution even in this scenario

Even when vendors have no domain-specific know-how and this AI solution can make or break your business, you may want to outsource it. Since this is a niche solution, you won’t find vendors with ready products. However, that is not the end. Read more