Artificial intelligence (AI) has been around for over 50 years and has greatly impacted many industries. In the near future, AI learning will continue to grow in both its scope and impact. The articles in this series will discuss the following: the history of AI, the current state of AI learning, its potential applications, and how AI learning will impact future generations.

While neural networks are an important component of artificial intelligence (AI) and machine learning research, most applications of AI do not use them. Most of them use standard machine learning models, such as linear/logistic regression and boosted decision trees. These models are behind a wide range of applications, from recommendation systems to ad targeting. Some of the most popular machine learning libraries are Google’s TensorFlow and the scikit-learn framework.

Cognizant’s Evolutionary AI model optimization
Cognizant’s Evolutionary AI engine optimizes business models for superior performance and accuracy. It can test millions of scenarios to find the best possible outcomes and is widely applicable to a variety of applications. It augments human decision-making, builds a predictive engine, and helps companies maximize business results.

Google’s investment in ML

The amount of money invested by venture capitalists into AI startups has tripled. Google has been leading the investment with incentives and grants for promising AI startups. While it’s hard to estimate the number of companies that choose to partner with Google, the company is clearly trying to encourage new and innovative companies to use its services.

Cognizant’s Synthetic genomes

Cognizant recently acquired Inawisdom, a startup in the UK and Netherlands that specializes in artificial intelligence, machine learning, and data analytics. The acquisition will provide Cognizant clients with end-to-end cloud-native AI solutions.

Backpropagation

Researchers have found that the backpropagation process can be useful for AI learning. The method works by using artificial neurons to simulate the firing patterns of biological neurons. It is similar to how neurons respond to rewards by firing more often. This reinforcement mechanism is called dopaminergic behavior. In addition to increasing the firing frequency of neurons, dopamine also strengthens connections between them. But unlike biological neurons, artificial neurons do not perform backpropagation with the same precision. They do it in other ways that approximate the same effect.

In conclusion, as AI learning continues to evolve, its potential for it becomes even more impactful and important. For businesses of all sizes, there is no doubt that increased AI capabilities will require increased investment in Research & Development (R&D), as well as continued innovation in how AI is used and taught.