Integrating IoT Analytics for a Smarter Future

By 2020, it is expected that there will be between 20 and 30 million IoT units in the marketplace, according to a study conducted by Gartner. With this much data being collected, the need for a way to analyze it grows exponentially. Many of the enterprise applications for IoT analytics, such as in manufacturing, finance, telecom, healthcare, and others have unlimited potential when data is managed and analyzed correctly.

To meet this need, IoT analytics has emerged as the broader category of uses and applications designed to help analyze the data obtained by IoT sensors. Once this data has been properly analyzed it can then be used to help make better, data-driven decisions for organizations that are in search of a competitive edge.

AI in Business: Comprehensive Guide to Transforming Your Company

You probably read tens of articles on AI in business mentioning numerous AI applications or exotic sounding algorithms like deep learning or support vector machines. But you don’t know what you can do with AI for your own business today. We have a solution:

First, AI is a tool and writing in general about AI in business is like writing about computers in business. It helps to be a lot more specific so we will break down AI applications by industry and business function to give you an overview of what AI can achieve. Or just take the shortcut, go to appliedAI.com and choose the industry of your business and the business function you work in to identify all AI use cases for your business. Read more

2018 AI predictions: Summary of top AI experts’ predictions

Since the beginning of the year, PwC, CEO of pymetrics, Gil Press  published predictions on the direction AI will take. We read them all and couldn’t resist adding our predictions and categorizing the predictions:

Mega-trends that will shape AI in 2018

The news cycle is full of AI, research centers being opened, re-organizations, new research findings and tabloids peddling that robots will kill us all tomorrow. Reading a different type of news everyday, it is easy to lose track of what is really happening. What are the major trends? Read more

Challenges of implementing an AI solution

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

Limitations of AI: Data hungry, opaque, brittle, self-reliant 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