Category: Artificial intelligence

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

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

Discover the ideal way to build your AI capabilities: build, outsource or buy

Discover the ideal way to build your AI capabilities: build, outsource or buy

Most CEOs are scrambling to use AI in their businesses as a company’s AI capabilities have the potential to become the ultimate competitive advantage. However, CEOs are faced with a difficult question: How to build AI capabilities.

Listening to your internal team can be confusing. Engineers will describe virtues of in-house solutions as it allows them to work on the next cool technology on your payroll. Commercial teams will ask for fast working solutions and won’t mind how much they cost since that cost will likely be booked under tech and not hurt the targets of commercial teams. Read more

Learning algorithms classified by their real world impact

Learning algorithms classified by their real world impact

At appliedai.com we are always talking about how AI systems are already changing the way companies are run, however it is easy to forget that we are still at the beginning of the AI revolution. We looked into the current and potential future economical impact of learning algorithms to understand how AI will evolve.

Explore today’s most impactful learning algorithms

Andrew Ng masterfully summarizes current commercial impact of learning algorithms in this 2 minute video. We summarize his main points below after also incorporating the topics highlighted by Yann LeCun’s famous cake. Read more

Measuring the advance of AI through competitive games

Measuring the advance of AI through competitive games

When we talk about how far Artificial Intelligence has come, we often use numbers to make our point. We build upon the previous work, and if we have done our job well, we end up taking an incremental step towards the future. However, what really changes our perception is seeing what our steps allow us to accomplish.

This week, just after Elon Musk’s OpenAI beat a professional human player at Dota 2 – a widely successful computer game that is also played as a competitive e-sport – we bring you two decades of advancement in Artificial Intelligence, and how it is slowly but surely conquering competitive games. Read more