Cognitive Computing = AI. Let’s not create another fancy term

Cognitive computing is another term for AI and cognitive analytics is analytics that leverages the latest advances in AI.

Wikipedia agrees with that. Wikipedia states that:

At present, there is no widely agreed upon definition for cognitive computing in either academia or industry.

And then goes on to explain that cognitive computing includes AI. We couldn’t agree more.

Though there is some verbiage around how cognitive differs from AI, we have not seen any rational  description of that difference. Vendors and some industry analysts and pundits make ill-defined distinctions between the 2 technologies but as a computer scientist and AI industry analyst, I can’t tell the difference. So unless someone comes along with a clear definition of how cognitive computing is different from AI, we act as though they are the same. Read more


AI Platforms in 2020: Guide to ML life cycle support tools

Research indicates that organizations have a hard time productizing machine learning models. AI platforms help businesses build, manage and deploy machine learning and deep learning models at scale. It makes AI technology more attainable and affordable by reducing software development work such as data management and deployment.

What is an AI platform?

An AI platform is a set of services that support the machine learning life cycle. This includes support for gathering and preparing data as well as training, testing, and deploying machine learning models for applications at scale. Read more


AI in analytics: How AI is shaping analytics in 2020

I started my career as a management consultant. Excel was our temple for analytics. For a recent graduate, macros and connected models perform miracles but albeit at great effort. However, our reach was extremely limited compared to the possibilities of today. We could not process anything with images, text, audio or video easily as non-technical users. Fast forward to today, citizen data scientists are unleashing machine learning on all of companies data to run diagnostic, predictive and prescriptive analytics. Read more


AI in Automation: Which tasks can we automate in 2020?

The advances in AI since the rise of deep learning can usher a new age of automation as machines go beyond human capabilities in a wide range of tasks.

The jobs that can be mostly automated include

  • predictable physical labor
  • white-collar back-office work: data collection and processing

Machines can now perform the activities involved in these jobs better/cheaper than humans. These activities include tasks that involve manipulating tools, extracting data from documents and other semi-structured data sources, making tacit judgments and even sensing emotions. In the next decade, driving is likely to become automated as well, enabling one of the most common professions to be automated. Read more


100+ AI Use Cases / Applications in 2020

Popularity of AI, machine learning and data science over the past few years

AI is becoming more integrated into our lives with more AI use cases emerging. This leads to increased interest in AI, its subdomains and related fields such as machine learning and data science as seen above.

According to a recent Gartner survey, 37% of organizations are still looking to define their AI strategies. To integrate AI into your own business, you need to identify how AI can serve your business, possible use cases of AI in your business. This article gathers the most common use cases covering marketing, sales, customer services, security, data, technology, and other processes: Read more