Category: Artificial intelligence

50 AI usecases / applications in 2018: In-depth guide

50 AI usecases / applications in 2018: In-depth guide

AI is changing every industry and business function. We are tracking the most impactful AI use cases here. This is meant to be a list that grows over time so feel free to contribute with your comments, this list is definitely not comprehensive now.

Marketing

Optimize Pricing & Placement

Pricing optimization: Optimize markdowns to minimize cannibalization while maximizing revenues.

Merchandising optimization:  Leverage machine learning and big data to optimize your online or offline merchandising.

Shelf audit/analytics: Use video, images or robots on the retail area to audit and analyze your use of shelf space. Read more

74 AI Conferences for Business Leaders in 2018 [Sortable]

74 AI Conferences for Business Leaders in 2018 [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

Sources: We were able to pull all this data together thanks to Topbots’ conference list and major conference organizers’ websites. Read more

How AI shapes consulting & creates 4 types of AI consultants

How AI shapes consulting & creates 4 types of AI consultants

Are you looking to use AI in your business and need to find the best AI consultants? Read on, we have compiled all that you need to know on AI consulting.

Artificial Intelligence will be one of the hot topics of 2018. Many organizations believe artificial intelligence has huge potential and would impact all the World. Currently, AI has created a huge Revenue potential for sales and marketing related topics, but moreover, this disruption is expected to other jobs. A recent Harvard Business Review article claims that in near future even the elite consultants face the risk of getting replaced by artificial intelligence. Read more

Dark side of neural networks explained

Dark side of neural networks explained

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