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For the last five years, we’ve been discussing the future of AI with many organizations, including Intel, at the forefront of that conversation. However, while ensuring the world gets to experience new heights and unparalleled technologies, organizations failed to notice that the future is upon us.
This realization dawned on me when I attended AI Devcon in San Francisco as an Intel Partner. The conference was hosted by several opinion leaders, such as Naveen Rao, the VP and GM of artificial intelligence at Intel. The conference helped me gain insight into the impact of our efforts and brought me to the realization that it is time other organizations jumped on the AI bandwagon initiated by Intel.
Why Do You Need to Start Using AI Now
There are multiple reasons you should start leveraging AI. The market presents a lot of potential for the future and set unprecedented records; that future seems bright. The current market growth potential for AI is high, and the market which currently stands at $3 Trillion is expected to grow to over $8 Trillion in the next five years. That is roughly a 200 percent growth over that period.
Besides the increasing market growth for AI, there is also a need to increase customer experiences. Customers now want the best experience possible and organizations that can offer those experiences will come out on top. Thus, if you want to ensure the right experience for your customers, I can’t stress enough the importance of jumping onto the AI bandwagon.
Examples of AI Implementation
AI is currently being implemented across a number of industries, and there are numerous use cases that set a standard when leveraging AI. Here we look at some of those use cases, and how organizations can pull from them for their own industries.
Improving Patient Outcomes
Medical data is extremely difficult to measure and analyze, which is why only a data science setup with the capabilities to host this complicated data can actually garner presentable insight. There are numerous cases of Intel working with medical institutions to improve patient outcomes through the collection and analysis of data. By gaining insight that was previously not available, medical experts can now use AI to give patients the right treatments at the right time. This helps improve patient outcome and increases the credibility of data science and AI tools.
Image Rendering in Filmmaking
The use of AI in image rendering for filmmaking improves user experience and ensures that everyone gets to witness a flawless experience while watching a film. AI can be used in filmmaking to increase the graphic representation of different living animals. The data from the movement and stimulation of animals is perfectly represented inside the film to create an honest representation of the movements made by living creatures.
Real-time AI Music
Real-time AI music is now a reality and Intel’s Movidius sits at the forefront of such advances. The technology has been credited with using set responses to add value to music and create a rhythmic tone. The model gathers insights and creates responses based on the frequencies of the content.
Machine Learning at AWS
Amazon has been leveraging machine learning to provide customers with suggestions and better understand their needs. Amazon is also using machine learning to create innovations in devices such as Alexa and Amazon Go. Amazon Sagemaker, which is at the forefront of Amazon’s machine learning initiative, brings machine learning to the cloud to benefit developers and enterprises.
Use of AI by Ferrari
The use of AI in a Ferrari is geared towards helping achieve the following functions:
- Helping drivers achieve faster times in race circuits.
- Helping engineers pioneer desired responses from the engine of the car.
These insights have been garnered through intelligent data sets achieved through drone technology.
What is the Basis of AI Machine Learning?
Machine learning is based on four types of learning:
- Supervised Learning: What we see in the world today is supervised learning, where machines are supervised and fed data tools required for garnering actionable insight.
- Transfer Learning: The transfer of knowledge you get from one insight into another data set. Transfer learning gives organizations the leverage they need to make machines learn from examples.
- Unsupervised Learning: Learning without the presence of able data. This means to learn without the presence of specific variables. Unsupervised learning has transformed the concept of machine learning.
- Reinforcement Learning: Reinforcement learning provides an infinite amount of experience and data. Reinforcement learning gathers actionable insight from that data. Model based reinforcement learning spurs from this method.
AI and Ethics
There will be a big discussion on ethics when AI starts making its own decisions. Whether these decisions comply with the human ethical standards that we currently follow is something that remains to be seen. We can take the example of a self-driven car in this use case. The car would have to make decisions, such as colliding into a passerby or not colliding into a pedestrian or going into a brick wall nearby. What’s interesting is that these decisions will be taken in real-time, and how AI gets to pull this ethical implication off is something that defines our future.
The growth of AI during the next 50 years can be envisioned. All that is needed to propel this growth forward is a solid infrastructure, software and facilities. The infrastructure will be provided by different hardware tools and the community with the assistance of developers. Once these developers have the necessary infrastructure, we will see a broader implementation of AI across the globe.
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