Challenges of implementing an AI solution [2020 update]

Deloitte survey identifies top challenges by corporations applying AI in their businesses

Judging from the numbers above which are from Deloitte’s 2017 State of Cognitive survey, it seems that only a tiny minority (6%) of the corporations are having a smooth ride with AI. We found the survey results realistic and combined them with our experience with companies that reached out to us regarding advice on their AI solutions. We think there are 2 classes of issues

Issues with building own AI solutions

Lack of business alignment

Identifying business cases for AI applications requires managers to have a deep understanding of current AI technologies, their limitations and the current processes of their division. As with any nascent field, lack of AI know-how in management is hindering adoption in most cases. Read more


AI Limitations in 2020: Data hungry, opaque, brittle systems

Though we preach that AI investments can transform businesses, we are also not naive in our beliefs in AI’s current capabilities. Most modern AI systems suffer from common issues highlighted by respectable publications that we will collect here:

Reliance on large volumes of data

Impacts deep learning algorithms. Sadly, even when data is available, it’s likely to suffer from bias.

Research on one shot learning is an attempt to solve this problem.

Reliance on labeled data

Limits supervised learning algorithms to relatively few problems where labeled data is either available or where the solution is so valuable that companies invest in preparing semi-manually labeled data. Read more


80+ AI Conferences for Business Leaders in 2020 [Sortable]

You want to learn more about how AI can help your business but you don’t want to spend your day in a coding event. You want to understand the business benefits, ROI, costs, implementation time of solutions.

You are in the right place! We prepared this sortable comprehensive list so you can easily sort by city or date or other parameters to find the right conference for your interest.

*If organizer frequently organizes AI events

Or you can skip the conferences and ask us to find the right AI solutions for your business: Read more


Dark side of neural networks explained

A simple neural network example

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


When should you build your own AI solution? [2020 update]

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