I’ve spent the last eight years working with AI, learning the ins and outs of building and applying AI solutions in business. After making countless mistakes, I created my own method for building and applying the technology. That was fine and dandy until the fall of 2022, when ChatGPT was released and gave a sudden rise in the usefulness and adoption of generative AI. For my consulting business TodAI, that meant a lot of new projects involving generative AI and a lot of learning. After several projects, I’ve identified places where generative models are clearly distinct from other AI when appl ..read more

AI comes in many different shapes and sizes. That applies to the use cases, the underlying technologies as well as the approaches to adopting AI in your organization. As many organizations are looking to adopt AI, an increasing need for tangible frameworks to understand the technology in a business perspective is requested by leaders in all industries.   Some of the key questions asked by leaders are simple. How much time and money is required to adopt AI and solve business problems via AI and what returns do we get for those efforts? That is more than reasonable questions but answering t ..read more

Lobe.ai just released for open beta and the short story is that you should go try it out. I was lucky and got to test it in the closed beta so I figured i should review a short review. Making AI more understandable and accessible for most people is something I spend a lot of time on and Lobe is without a doubt right down my alley. The tagline is “machine learning made simple” and that is exactly what they do. Overall great tool and I see it as an actual advance in the AI technology by making AI and deep learning models even more accessible than the AutoML wave is already doing. So what is L ..read more

Artificial intelligence is becoming mainstream and many organizations, startups and big corporates alike, are now starting internal AI projects or are incooperating AI into other existing IT. There's just one problem. Leading AI projects are very different from leading in traditional IT.  As a result many AI projects either fail or ignite frustration with project participants, users of the AI and the management involved. They are unaware that they are now in a new paradigm and should have different expectations than usually have to IT. Even implementing off-the-shelf AI components or sys ..read more

Being data-driven is usually used and understood with positive connotations but when I hear the word used I get a little anxious about the “data-driven” decisions that might be about to happen. Let me explain why. According to Wikipedia data-driven means “The adjective data-driven means that progress in an activity is compelled by data, rather than by intuition or by personal experience.” In other words - Look at the data as a primary source of information to act on. When the data gives you a reason to act, you act. It might at a glance seem like a very sound way to work and especially in the ..read more

Introduction   I have been looking for a list of AutoML solutions and a way to compare them, but I haven’t been able to find it. So I thought I might as well compile that list for others to use. If you are not familiar with AutoML read this post for a quick introduction and pros and cons.   I haven’t been able to test them all and make a proper review, so this is just a comparison based on features. I tried to pick the features that felt most important to me, but it might not be the most important for you. If you think some features are missing or if you know an AutoML solution that ..read more

Artificial intelligence(AI) projects are becoming commonplace for big business and entrepreneurs alike. As a result many people with no prior experience with AI are now being put in charge of AI projects. Almost 5 years ago that happened to me for the first time and I’ve since learned a lot. So here’s six things I wish I had known, when I did my first AI project. 1. Data is the most expensive part AI is often talked about as being technically very difficult requiring extensive resources to develop. But in fact that’s not the complete truth. The development can be costly but the vast majority o ..read more

1. Transfer Learning Transfer learning is used a lot in machine learning now since the benefits are big. The general idea is simple. You train a big neural network for purposes with a lot of data and a lot of training. When you then have a specific problem you sort of “cut the end off” the big network and train a few new layers with your own data. The big network already understands a lot of general patterns that you with transfer learning don’t have to teach the network this time.  A good example is if you try to train a network to recognize images of different dog species. Without tran ..read more

There’s no definitive or academically correct definition of artificial intelligence. The common ones usually define AI as computer models that perform tasks in a human like manner and simulate intelligent behaviour.  The truth is that for each expert on the field of AI you ask about the definition, you will get some new variation of the definition. In some cases you might even get a very religious schooling on how it’s only AI if the system contains deep learning models. Do you ask many startup founders if their systems contain AI even though it’s just a very simple regression algorithm ..read more

Collecting data is for many AI projects without a doubt the most expensive part of the project. Labelling data like images and text pieces is hard and tedious work without much possibility of scaling. If an AI project requires continuously updated or fresh data then this can be a high cost that can challenge the whole business case of an otherwise great project. There are a few strategies though to lower the costs of labelling data. I have previously written about Active Learning; a data collection strategy that focuses on prioritizing the labelling of the most crucial data first given the mo ..read more