How the Big Players Are Deploying AI

Big Players and Artificial Intelligence

While the last couple of years has undoubtedly been difficult for all types of businesses, it didn’t slow down development within the artificial intelligence (AI) and machine learning (ML) space

According to IDC, as much as 65% of organizations have accelerated the use of digital technologies this year. In this case, technologies like AI will transform existing business processes to boost employee productivity, drive customer engagement, and enhance business resiliency.

This is hardly unexpected because AI and its subset ML is a big deal. As such, companies continue to invest heavily in this technology. It comes as no surprise that the industry giants are leading the way.

Data is the lifeblood of companies like Amazon, Facebook, and Google, which run their operation almost entirely online. So, they have the most data and can profit the most from it if they use their data and smart algorithms wisely.

With these oceans of data, for example, they can come up with new products and services that we never imagined before, improve customer engagement, or optimize processes to boost efficiency. All this together gives tech giants a competitive advantage in the marketplace.

They are at the frontlines as they deal with larger volumes of data and have the necessary resources to research and innovate. So, what exciting products and services are they building with AI? How will it transform their businesses and the industry as a whole?

We all know about AI’s role in placing ads in Google Search and YouTube, translations, and how Amazon uses smart algorithms to power product recommendations. How else are the big players using AI, ML, and deep learning (DL) to transform their offerings? Let’s dive right in.

Facebook’s Anticipative Video Transformer

Last October, Facebook (nka Meta) announced the release of their new ML-process, anticipated video transformer or AVT. This cutting-edge technology can predict future actions purely based on visual interpretations.

This is a progression of the company’s projects dedicated to smart algorithms that continuously train using publicly available videos. As such, AVT follows an end-to-end attention-based model that is a new model driven by recent breakthroughs in transformer architectures.

This is especially true in natural language processing (NLP) and image modeling for apps ranging from augmented reality (AR) to connected self-driving vehicles.

It works by analyzing activity and predicting the possible result. As such, the company plans to leverage this technology to work across apps in its metaverse. It will also enable access to others through APIs that communicate with each other.

Based on the vision transformer (VIT) architecture, AVT splits the frame into patches that don’t overlap. It then embeds them with a feedforward network and appends a classification token. This is then applied to various layers of multi-head self-attention.

The head architecture can take pre-framed features and then apply another transformer architecture with casual attention. By doing so, this model can depend on only past features when generating a new representation of any of these individual frames.

Going forward, expect to see AVT in AR tools, AI assistants, and more. We can also expect to see similar technologies follow suit.


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Google’s Vertex AI, CCAI, and DocAI

Last May, Google released Vertex AI, a comprehensive platform for all your favorite ML tools. It was a significant release as Vertex AI helps data teams quickly build, deploy, and maintain multiple ML models.

What exactly is Vertex AI? At its most basic, Vertex AI uses AI-driven pipelines to build Auto-ML classification end-to-end workflows. This approach helps optimize reinforcement learning and more.

Unlike older competing platforms that demand at least 80% fewer lines of code to train a model, Vertex AI enables machine learning operations (MLOps). It’s also an excellent solution for ML veterans and beginners alike.

Google Cloud’s Contact Center AI (CCAI) helps enterprises, especially call centers with limited AI capabilities, discover insights about their customer and partner from relationships and interactions. It also allows organizations to deploy virtual agents that behave and chat naturally.

Document AI (DocAI), on the other hand, takes the guesswork and physical labour out of document processing. This means it helps teams better understand and capture data in documents to optimize and streamline workflows.

Apple’s AI-Powered Apps

It’s safe to say that anyone who has owned an Apple device has come in direct contact with intelligent algorithms. Some of the standard AI-powered technologies we have come across in iOS, macOS, watchOS, and iPadOS are the following:

  • App Library suggestions
  • FaceID
  • Handwash detection
  • Handwriting recognition
  • Native sleep tracking
  • Sound recognition
  • Translate app

While Apple wasn’t at the forefront of the AI revolution, the company has worked energetically to make up for the lost time. With oceans of data available at their fingertips, Apple has made the most of it by training its algorithms to serve its customers better.

However, as the company keeps its goings-on under wraps, we don’t exactly know how extensively they are using data and how these AI-powered apps work. Furthermore, we only find out about their latest innovations right before it’s released (so, expect to see more AI-powered tools this year!).

But it’s safe to safe that Apple devices capture valuable data that enable crucial insights on user behavior and much more. The tech giant then uses this information to develop cutting-edge smart products.

IBM’s Watson

In recent years, IBM has transformed old business models with ML to access new revenue streams. While the company was old and at risk of becoming irrelevant, the adoption of AI and the emergence of the AI-assistant Watson cemented the company’s future.

Watson, popular among healthcare institutions and retailers, offers highly accurate recommendations. This is especially true when it comes to the treatment of certain types of cancers.

But it doesn’t stop there as Watson can do a lot more and even help businesses attend to their customers’ needs. If that wasn’t enough, Watson also shared insights at the US Open. At the event, IBM’s Power Rankings and Match Insights were driven by Watson.

Over the next 11 months, we can expect more innovation from these innovators when it comes to AI and ML. As such, we can expect to see more cutting-edge tools we never thought of before and possibly new ways for smart algorithms to change our lives.



Andrew Zola