Artificial Intelligence Is Electricity 2.0

Here are a few examples of applications that leverage AI in some permutation or another. This is by no means a comprehensive list. After the bulleted list we link to TEK2day articles that cover AI.

Tickers mentioned: AAPL, ADBE, AMZN, ANSS, AYX, BAC, CERN, CLDR, CRWD, FB, GOOG, IBM, ICE, JPM, MSFT, ORCL, PBI, PEGA, SNOW, SSNC, VRSK, ZEN

Voice-Powered Applications: Google Assistant and Amazon’s Alexa are the two preferred voice recognition engines that power everything from smart home devices & systems (security, automobile…) to phones and more. Microsoft is also big in the conversational AI game.
Language Applications: Text-to-Speech, Speech-to-Text and Translation applications leverage neural networks to power a variety of use cases. The footprint of these deep learning models has shrunk dramatically. Models no longer need to reside at the edge of the network and in many cases are incorporated directly into applications – especially in Google’s case.
Facial & Image Recognition: Photo apps, payment apps, social media filters, deepfake applications and various enterprise applications (ex. Amazon Rekognition) leverage this advanced technology.
Predictive Analytics: Many predictive analytics applications leverage machine learning. The use cases are too numerous to capture completely, but everything from predicting retail sales, weather, customer churn, flood damage, wine and movie recommendations, social media topics of interest and more leverage advanced analytic capability. These discrete examples are often combined for various use cases. For example:
- Retail site selection models incorporate population and demographic data, traffic patterns, weather patterns and more to determine how a particular location may evolve over a multi-year period.
- Autonomous driving platforms incorporate traffic patterns, computer vision and LiDAR systems to inform the platform of its real-time surroundings.
- Predicting health outcomes from patient-specific diagnostics and aggregated, anonymized historical patient data. I believe that AAPL, AMZN and GOOG will own this space at CERN and Epic’s expense.
- Many firms use a combination of NLP, OCR, RPA and ML to automate document-heavy or research-intensive processes such as mortgage applications, insurance claims, medical records, fraud detection, legal discovery and document research to name a few examples.
  - Some of these products are used by millions of users and are produced by well-known firms such as Adobe and Microsoft.
  - Other products are equally powerful but have narrower use case and have been developed by lesser known firms such as Alteryx, Ansys, Automation Anywhere (pvt.), CrowdStrike, DataRobot (pvt.), Ellie Mae/Intercontinental Exchange, ESRI (pvt.), Facebook, MapInfo/PBI, Oracle, Pegasystems, SAS (pvt.), Solera Holdings (pvt.), SPSS/IBM, SS&C Technologies, Verisk, Zendesk and countless others.
  - A third product category consists of companies that are not explicitly Software developers nor “Analytics” companies but develop AI-powered products and services to drive operational efficiency. Most of the big banks such as Bank of America and J.P. Morgan have incorporated broadly-defined AI into their operations. Similarly, most if not all of the large insurance companies have Data & Analytics departments that leverage various Advanced Analytics products and tools including those from Cloudera, DataRobot, SAS and Snowflake to build proprietary models and processes.
  - Some of the above products incorporate core AI/ML capability from AWS, Azure and GCP. Others do not.

Related TEK2day articles that cover AI & ML:

Here are a few examples of applications that leverage AI in some permutation or another. This is by no means a comprehensive list. After the bulleted list we link to TEK2day articles that cover AI.

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