What's next for an organization after Digital Transformation?? Use AI/ML and get more value from the invaluable data your org has generated!

As businesses and other organizations undergo digital transformation, they’re faced with a growing tsunami of data that is at once incredibly valuable and increasingly burdensome to collect, process and analyze. New tools and methodologies like AL/ML are needed to manage the vast quantity of data being collected, to mine it for insights and to act on those insights when they’re discovered.

AI/ML (artificial intelligence (AI) and machine learning (ML))—represents an important evolution in computer science and data processing.

What is AI?

Artificial intelligence refers to processes and algorithms that are able to simulate human intelligence, including mimicking cognitive functions such as perception, learning and problem solving. Machine learning and deep learning (DL) are all subsets of AI.

Applications of AI include search engines, personal assistants that understand spoken language, self-driving vehicles and recommendation engines, such as those used by Netflix or Instagram.

There are four levels or types of AI—two of which we have achieved, and two which remain theoretical at this stage – (1) reactive machines, (2) limited memory, (3) theory of mind and (4) self-awareness.

1. Reactive machines: no "learning" happens the system is trained to do a particular task or set of tasks and never deviates from that. Example: Google’s AlphaGo AI, etc.
2. Limited memory AI systems are able to store incoming data and data about any actions or decisions it makes, and then analyze that stored data in order to improve over time. This is where "machine learning" really begins, as limited memory is required in order for learning to happen. Since limited memory AIs are able to improve over time, these are the most advanced AIs we have developed to date. Examples include self-driving vehicles, virtual voice assistants and chatbots.
3. Theory of mind is the first of the two more advanced and (currently) theoretical types of AI that we haven’t yet achieved. At this level, AIs would begin to understand human thoughts and emotions, and start to interact with us in a meaningful way. Here the relationship between human and AI becomes reciprocal. The "theory of mind" terminology comes from psychology, and in this case refers to an AI understanding that humans have thoughts and emotions which then, in turn, affect the AI’s behavior.
4. Self-awareness is considered the ultimate goal for many AI developers, wherein AIs have human-level consciousness, aware of themselves as beings in the world with similar desires and emotions as humans. As of today, self-aware AIs are purely the stuff of science fiction. Example: Terminator ??

What is ML?

In a nutshell, machine learning is a subset of AI that falls within the "limited memory" category in which the AI (machine) is able to learn and develop over time.

There are a variety of different machine learning algorithms, with the three primary types being (1) supervised learning, (2) unsupervised learning and (3) reinforcement learning.

1. Supervised learning is simplest and is when an AI is actively supervised throughout the learning process. Researchers or data scientists will provide the machine with a quantity of data to process and learn from, as well as some example results of what that data should produce. The result of supervised learning is an agent that can predict results based on new input data. The machine may continue to refine its learning by storing and continually re-analyzing these predictions, improving its accuracy over time. Supervised machine learning applications include image-recognition, media recommendation systems, predictive analytics and spam detection.
2. Unsupervised learning involves no help from humans during the learning process. The agent is given a quantity of data to analyze, and independently identifies patterns in that data. This type of analysis can be extremely helpful, because machines can recognize more and different patterns in any given set of data than humans. Like supervised machine learning, unsupervised ML can learn and improve over time. Unsupervised machine learning applications include things like determining customer segments in marketing data, medical imaging, and anomaly detection.
3. Reinforcement learning is the most complex of these three algorithms in that there is no data set provided to train the machine. Instead, the agent learns by interacting with the environment in which it is placed. It receives positive or negative rewards based on the actions it takes and improves over time by refining its responses to maximize positive rewards. Some applications of reinforcement learning include self-improving industrial robots, automated stock trading, advanced recommendation engines and bid optimization for maximizing ad spend, etc.

What is DL?

Deep learning (DL) is a subset of machine learning that attempts to emulate human neural networks, eliminating the need for pre-processed data. Deep learning algorithms are able to adopt, process and analyze vast quantities of unstructured data to learn without any human intervention.

As with other types of machine learning, a deep learning algorithm can improve over time.

Some practical applications of deep learning currently include developing computer vision, facial recognition and natural language processing.

Why AL/ML?

AI/ML has the potential to transform all aspects of a business by helping them achieve measurable outcomes including:

• Increasing customer satisfaction
• Offering differentiated digital services
• Optimizing existing business services
• Automating business operations
• Increasing revenue
• Reducing costs

AL/ML Applications in Healthcare:

1. AI-assisted radiology and pathology

• identifying suspicious spots on the skin, lesions, tumors, and brain bleeds.
• Identifying rare or difficult to diagnose diseases – identifying patterns, routines
• ML tools are also adding significant value by augmenting the surgeon’s display with information such as cancer localization during robotic procedures and other image-guided interventions.
• AI-enabled platforms, able to connect to a multitude of patient databases and to analyze a complex mixture of data types (e.g. blood pathology, genomics, radiology images, medical history)
• Ability to translate and visualize their finding to human-intelligible forms so that doctors and other healthcare professionals can work on their output with high confidence and complete transparency.

2. Physical robots for surgery assistance

• Surgical robots can provide unique assistance to human surgeons, enhancing the ability to see and navigate in a procedure, creating precise and minimally invasive incisions, and causing less pain with optimal stitch geometry and wound.
• Collaboration of robots with the aid of massive distributed processing
• Data driven insights and guidance based on surgery histories (performed by both machines and humans)
• AI-generated virtual reality space for real-time direction and guidance
• Possibility of telemedicine and remote surgery for relatively simple procedures

3. Drug discovery with the aid of AI/ML techniques -?Unlike the conventional longer process, AI techniques are increasingly being applied to accelerate the fundamental processes of early-stage candidate selection and mechanism discovery.

4. Precision medicine and preventive healthcare - Going beyond the prediction and modeling of the disease and treatment, such an AI-system can also potentially predict future patient’s probability of having specific diseases given early screening or routine annual physical exam data.

5.?AI for public health system - Such powerful techniques can be applied to large-scale public health systems along with individual patient care.

Conclusion

AI/ML is the future for organizations who are undergoing Digital Transformation or are already transformed!

I see this space becoming bigger and opening up innumerable opportunity for leveraging data and driving insights!

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