Machine Learning (ML) and Artificial Intelligence (AI). Terms we always hear about, that sound good, but seldom do we know what they really mean or where are they used to good benefit.
Regardless the difference, there needs to be sufficient change management within your organization and a clear-cut business case for AI, and ML .
Not just because it is fashionable to do so. It could break your bank account.
And we are still at a stage where a machine learns as long as a human is there to teach it.
We debunk this here.
AI and ML: One and the same?
Let’s lay to rest any confusion about ML and AI. Forbes reported on this saying ML is actually a subset of AI, a massive of its own.
“Machine learning is a method of analyzing data using an analytical model that is built automatically, or ‘learned’, from training data,” said Rick Negrin, who is the VP of Product Management at MemSQL. “The idea is that the model gets better as you feed it more data points.”
With ML, you first have to collect and train a vast amount of data before operationalizing the machine learning, such as by using it to help provide insights that require very simple and repetitive tasks at large scale.
“Successful machine learning is only as good as the data available, which is why it needs new, updated data to provide the most accurate outputs or predictions for any given need,” said Panagiotis Angelopoulos, who is the Chief Data Officer at Persado.
How does AI fit in all this?
AI applications that use ML can take data and quickly turn it into actionable information.
According to SearchEnterprise AI can be categorized as either weak or strong. Weak AI, also known as narrow AI, is an AI system that is designed and trained to complete a specific task. Industrial robots and virtual personal assistants, such as Apple’s Siri, use weak AI.
Strong AI, AKA artificial general intelligence (AGI), describes programming that can replicate the cognitive abilities of the human brain.
“A” team behind AI and ML
ML will need data scientists to interpret and manipulate data but when deploying new solutions will also require product managers, software engineers, data engineers, operational experts to develop process and operational workflows and a host of interdependent others.
Expensive? Yes. It’s why off-the-shelf solutions may be best and many are out there and inexpensive.
Functional uses for AI/ML
BusinessWire says AI and ML have become essential for the food delivery industry, as increasing competition to improve customer retention rates and product quality has forced the companies to explore big data, AI and ML.
According to Quantzig, ML helps to understand the customer behavior better and provide services as per their preferences and analyze factors like the impact of traffic, temperature and others that impact consumption and delivery trends.
The food delivery industry also uses chatbots to enhance customer relationships.
According to Fortune, preserving patient privacy in most datasets used for healthcare is thanks to AI which helps anonymize the data.
Personal identifying information such as names, addresses, phone numbers, and social security numbers is simply stripped out of the dataset before it is fed to the AI algorithm. Anonymization is also the standard in other industries, especially those that are heavily regulated, such as finance and insurance.
Artificial intelligence (AI) plays a crucial role in the future of this industrial automation — much of the advancements in machine learning are made possible through a secured production environment.
These include self-learning robots and “cobots,” environmental monitoring in factory automation, operations and process management with AI-based smart glasses, as well as edge computing and intelligent sensors.
Fraud accompanied the boom in digital services and the highly dynamic environment is where fraudsters are discovering new attack vectors every day.
AI/ML approaches can help by spotting patterns in previous fraud cases and using them to detect suspicious behavior by customers, employees or systems.
Digital banks such as Monzo are using smartphone cameras with ML powered facial recognition technology to prevent unauthorized users from gaining access to customers’ accounts via their mobile apps.
Facial recognition can even detect when a person is sleeping or unaware that the camera is being used, potentially making them a much more powerful access control measure than traditional password-based login methods.