Myths Debunked

Study Notes

5 Myths of AI and Machine Learning Debunked

AI and machine learning have been around for over 60 years but are still misunderstood today.
Weak AI, also known as narrow AI, relies on algorithms and programmatic responses to simulate intelligence for a specific task.
Strong AI, also known as true AI, is designed to think on its own, learn and adapt, and make decisions that are not programmatic in nature.
Machine learning is a specific type of AI that allows a computing device to learn from a store of data and draw conclusions from it.
AI is not a magic wand and requires humans to define the problem, identify an appropriate AI technology to solve it, train the tool with the correct data, and verify the results.
AI and machine learning tools are becoming increasingly accessible to anyone willing to learn, and many are open source.
Even small businesses and environments limited in scope and scale can benefit from the lessons provided by AI and ML.
Businesses do not need to have all the data in-house for AI to be useful as tools can collect data from external sources.
McKinsey predicts that by 2030, 375 million workers will need to "switch occupational categories" as machines become increasingly capable, but Gartner predicts that 2.3 million new jobs will be created by 2020.
Most AI is weak AI and is not designed to replicate the abstract ways that a human thinks and works.
AI is not a cure-all and requires careful management over time to avoid running off the rails.
The key to successfully building an AI outcome is training, and it is not just a game of volume but one of quality too.Debunking 5 Myths of AI and Machine Learning
AI and machine learning are already providing substantial benefits to businesses, including enhancements of product features, better decision-making, and new product creation.
Deloitte’s survey of 250 early adopters of AI found that 83% achieved moderate or substantial benefits from their work with these technologies.
Cognitive technologies are expected to transform businesses within three years or less, with 63% of business leaders believing that pressure to reduce costs will require the use of AI.
While AI has its limitations and can make catastrophic mistakes, it can also enhance and elevate the work of human operators who are required to train the AI tool and ensure its results are on target.
AI is not a technology that’s decades away from making an impact, as investments in AI and machine learning are paying off, and even pilot projects are turning in early, positive returns.
AI is not a blanket tool, and businesses need to determine where to target AI based on specific challenges in their organization.
Numerous tools on the market allow businesses to experiment with AI in a sandbox targeting small “problem areas” that might have long stymied attempts at improvement.
Overcoming hurdles such as educating nervous staff members and showcasing how AI can improve their work lives can help businesses implement AI and machine learning.
Smaller businesses may need to overcome the sentiment that AI is a game that only the largest enterprises can play, which is where targeted pilot projects can help.
AI is already having a profound impact on businesses by improving customer satisfaction, decreasing manufacturing downtime, and increasing worker productivity.
AI is not a science project but rather a basic business requirement that businesses need to develop a strong strategy for, or risk getting left behind in the market.
While AI’s most far-reaching concepts are years away from reality, AI tools can already win at Jeopardy!, poker, and chess, detect breast cancer, and log tens of thousands of miles behind the wheel of self-driving vehicles every day.