Hello, readers, and welcome to the first edition of AI for Business ! Each week, we will be posting 5 interesting reads around Artificial Intelligence for businesses, where we will share with you some interesting news about the development of Artificial Intelligence and how companies use Artificial Intelligence. Our objective: give you actionable insights and examples of how AI can be used by businesses
This week you will learn about: the initiatives that countries take towards AI, the challenges to overcome in order to implement AI, some AI use cases on climate forecasting and the detection of fraudulent use of passwords.
FIGURE OF THE WEEK
4% : this is the proportion of companies that are actively using AI. According to a study commissioned by Microsoft as an outlook on 2019, there is a large discrepancy between the percentage of companies that are still in the planning or piloting stages of implementing AI (64%) and the companies that are actively using AI in ‘many processes and to enable advanced tasks’(4%). But 71% of companies consider AI an important topic on the executive level. What about you?
THE RISE OF AI
- National AI strategies in the world's most powerful economies - By Thomas Macaulay for Techworld
This article explains how every powerful nation starts to understand the importance of investing in AI in the hope to take the lead, but they all have different AI strategies. For example, countries like Germany and the UK are focused on using the funding for new R&D centres and new investment in universities that cover AI topics. On the other hand, India wants to focus on social good and EU on improving consumer trust in AI.
AI FOR EXECUTIVES
- AI Deployment Challenges: 5 Tips To Help Overcome The Hurdles - by Daniel Newman for Forbes
The article looks at the reasons why AI is mostly being used by some forward-thinking companies and there is no full commodification of AI yet. In order to step up the AI game, some hurdles should be taken care of. One of the challenges mentioned is the need for a mindset shift about AI. AI should not be seen as something that is only important for the IT department. AI is also a business responsibility, meaning that business executives need to have a clear view of the business challenges they are looking to solve, and the technologies that can use to answer these challenges.
- Why Businesses fail at machine learning - by Cassie Kozyrkov for Hacker Noon
The author makes a comparison between the 2 types of Machine Learning, Machine Learning Research and Applied Machine Learning, by using a clear example of a baker and his oven. The author argues that decision-makers should make the right choice when hiring Machine Learning specialists so they have the right skills to craft the Machine Learning solution that responds to your needs.
Here at Kantify, we bring the business expertise and the technical expertise together from step 1: we ensure that your ‘kitchen’ is perfectly tailored for the chef and do not reinvent the oven!
AI IN PRACTICE
- Using Machine Learning to improve sub-seasonal climate forecasting - by Taylor De Leon for MIT news
We’ve heard many times that climate is hard to predict. Yet this article shows the prediction possibilities of a state-of-the-art Machine Learning Algorithm. The engineers working on this project explain that by using Machine Learning techniques on historical data they are able to help energy companies and cities prepare for a severe storm much farther in advance than current predicting models.
- Sharing passwords for a video streaming site? This company will use AI to track you down - by James Vincent for the Verge
A UK company, Synamedia, has made it its speciality to track down shared passwords. Shared passwords are a real issue for streaming sites such as Netflix, HBO or Hulu. Synamedia has made a platform that can give their client a prediction score, based on the historical activity of end users, on how likely it is that this end user is using a shared account. Machine Learning is perfectly suited for this type of assignment at it can continuously learn to detect fraudulent patterns that are invisible to the human eye.
That’s it for this week! We are planning to make this a weekly update on the latest reads to look out for. Feel free to give your opinion on this format and check our other articles in our Articles section.