10 Eye-Catching Evolutions that Made a Buzz in the AI Community!

10-Eye-Catching-Evolutions-that-Made-a-Buzz-in-the-AI-Community!

10-Eye-Catching-Evolutions-that-Made-a-Buzz-in-the-AI-Community!

AI Evolutions helps predicts improved business integration and increased democratization

AI has evolved into a powerful tool in recent years, allowing machines to think and act like humans. Furthermore, it has attracted the attention of tech companies all over the world and is regarded as the next significant technological shift following the evolution of mobile and cloud platforms. Some even refer to it as the “fourth industrial revolution.”.

Businesses that use AI and related technologies such as machine learning and deep learning to uncover new business insights. Artificial intelligence is a branch of computer science dealing with the simulation of intelligent behavior in computers. People frequently use machine learning, deep learning, and AI interchangeably. Deep learning, on the other hand, is a subset of machine learning, which is a subset of AI. Knowledge representation was required for machines to interact with the real world in the same way that humans do, machines needed to be able to recognize objects, people, and languages. The programming language Lisp was created specifically for this purpose.

Emotional intelligence, intuition, and creativity were all examples of General Intelligence. All of these objectives lay the groundwork for creating a machine with human capabilities. Millions of dollars were spent to make their vision a reality. However, the US government quickly realized the lack of powerful computing technologies required to implement AI. The high processing power of today’s tiny silicons has made AI feasible in the current context, allowing for the development of improved algorithms. The democratization of AI knowledge began when world-class research content was made available to the general public. Data and computing power (cloud and GPU) that made AI accessible to the masses without requiring a large upfront investment or the status of a mega-corporation.

Even if you had access to data and computing power, you had to be an AI expert to use it. However, there was a proliferation of new tools and frameworks in 2015 that made exploring and operationalizing production-level AI accessible to the general public. You can now build on the shoulders of giants such as Google (Tensorflow) and Facebook ( PyTorch). With the democratization of AI, numerous organizations such as FastAI and OpenAI have emerged.

AI as a service has taken this a step further in the last two years, allowing for easier prototyping, exploration, and even the incorporation of sophisticated and intelligent use-case-specific AI’s into the product. AI as a Service is provided by platforms such as Azure AI, AWS AI, Google Cloud AI, IBM Cloud AI, and many others.

2007- NVidia launches CUDA, a set of tools that allowed application developers to write programs to run on NVIDIA’s hardware.

2009– AI researchers discover GPU for Deep Learning

2010– Democratize Data Access begins – Kaggle – Data Science and ML competitions and ImageNet Large Scale Visual Recognition Challenge started to democratize data for Image Recognition.

2011– Democratize Machiner Learning Knowledge begins -Andrew NG launches online course for Machine Learning in Coursera and Sebastian Thurn in Udacity

2012- Google Brain Team finds cat videos from millions of Youtube Videos using GPU and Deep Learning

2015– Open source frameworks such as TensorFlow and Keras releases, OpenAI starts to ensure that artificial general intelligence benefits all of humanity

2016- Started Machine Learning as a service by companies such as Google, Amazon, Microsoft

2017- Caffe releases

2018– Fast.ai release deep learning library, Google releases Tensor Processing Unit (TPU),

2019– The proliferation of AI continues

More businesses are attempting to use AI to solve problems and develop an AI strategy within their organizations. The journey of AI, which began with six goals, is now being completed gradually. A future in which humans and machines coexist appears to be possible as technology advances. Now we have the option of either observing the trend or planning our AI strategy to make an impact in a technologically driven world.