AI / ML – Past, Present Future – Part 3a

Just a recap, we learnt in my last update the components, success criteria and impact of the new age machines that are learning and thus by utilizing AI/ML mould the world we are going to experience. In this excerpt we are going to quickly look through the making of Data that makes an AI system successful and then dwell upon the design and delivery of Digital business models and solutions thereof in part b of this third post.

As we learnt earlier, each industrial revolution has been catalyzed by a new raw material: Coal, Steel, oil or Electricity. This time around, data is the primary raw Material. Today organizations are able to know precise information on a varied amount of genres – right from how their engine is performing during a particular journey to a specific student’s performance on one of the lessons in a class. All this is possible because of the Data they have access to.

The Amount of data that each organization does collect is humongous and more often than not organizations don’t know what to do with such data. Just reiterate that this is not new for any of the industrial revolutions, during the time when industries were based on Coal, industry was just about seeing the presence of Oil. However in the initial stages Oil was more or less looked upon as an obstruction to Coal mining and industries didn’t know what to do with Oil (Sticky, black, mucky liquid). This happened only till the time when James Young (Scientist) applied a distillation process to efficiently covert the mucky oil into more useful stuff – refined Oil. Today many of these business decision makers are struggling with similar re-conceptualization of their Data. Hence, what will differentiate organizations in the future are the ones who become masters in consistently turning this abundant data into actionable, and proprietary, insight.

So by contrast, the new decision making leaders of today are more relying on ‘data-first’ as their data is not sticky or mucky but the lifeblood of the new machine, the fuel that moves it forward. In sticking with our oil comparison again, data is superior in most senses to Oil. While data lies at the core of our new machine, maintaining a healthy supply chain of this raw material is important to any industrial revolution. Let us look at the 3 key activities that apply here Harvesting of Oil, Refining of Oil and Distribution of Oil. Let us apply the same to the Data world. 

Thus this three pronged approach is a useful way of thinking about organizing your technology, staffing and approach to building your own new machine. Let us now also spend a little time on how to give Meaning to the Data which is such an important core of the AI Framework

Going through time, its imperative that the 3 steps above will become a universal practice. Harvesting will more or less stabilize and distribution will have nothing to do with improvement of the data. Thus the middle step of refining or turning data meaningful will be the key competitive battleground. This is where organizations will need to convert the data into insight and apply that insight via a new commercial models, and this is where Business Analytics comes into the frame.

Business Analytics can be defined as the tools, techniques, goals, analytics, processes and business strategies used to transform data into actionable insights for business problem solving and competitive advantage. All organizations that could harness values from data better than others can enjoy an average cost decrease of about 8,1% and an average revenue increase of about 8.4%.

So what do you want your analytics engine to analyze? And there comes the Code and stuff for all of us to look at. We need to turn everything – really everything with instrumentation- with sensors you begin the process of harvesting all the data in your organization but you will also greatly increase the intrinsic value of the very objects you are instrumenting. For instance, the value of a sensor fit car seat which can adjust its height and inclination based on the passenger who travels in it, greatly increase when compared to the static car seat. The sensors being referred to here can just take any shape and size in that context of the car seat like providing temperature data and Lighting conditions etc. If you take this mundane example of something ‘Static’ becoming ‘Dynamic sensor fit’ and extrapolate it into the ‘universe of things’, the impact of instrumentation becomes more and more profound.

This is why The LGs and Samsungs of the world are making the so called Dynamic/Smart Refrigerators and Televisions. It’s also why there an explosion of activity around the idea of instrumenting people. Let’s face it, at times we are pretty ‘static’ too. Instrumenting us will help us get smarter about a lot of important things, like our health as well. We need to therefore now become data analysis savvy so as to prepare for the next wave of AI based systems that can help / support our lives and make earth a better place to live in.

Stay tuned…. Part IIIb of this foray, we will dwell upon the Digital Business Models and Solutions: – “Robots” that outline the design and delivery of the AI platform.

Please feel free to review my other series of posts 

Authored by Venugopala Krishna Kotipalli

AI / ML – Past, Present & Future – Part 2

Just a recap, we learnt in my last update, the advent of the industrial revolutions, and the current industrial revolution of the ‘Machines’ we are currently experiencing. The impact of the new age machines that are learning and thus by utilizing AI/ML mould the world we are going to experience. In this excerpt we are going to understand the machine in itself, the raw material that constitutes it and how the world of AI/ML comes alive.

New Machine: A system of intelligence that combines software, hardware data and human input:

  • Software that learns
  • Massive hardware processing power
  • Huge amounts of data

Any New machine exhibiting AI has three main Elements

1. Digital process Logic

  • Transform many manual processes into automated ones
  • Car dispatch process between traditional vs Uber
  • Digitized process multiplied over millions of transactions – an industry is revolutionized – structuring the process is the hardest part
    • thereby new Large Databases that are stable, scalable and tested. For e.g. Hadoop are finding favor against Oracle/SAP

2. Machine Intelligence

  • Combination of algorithms, automation processes, machine learning and neural networks – just a richer data set – HEART
    • thereby Highly efficient and always on plumbing

3. Software Ecosystem

  • Multiple systems of intelligence connected thru API. For e.g. Uber uses Twilio for cloud communications, Google for maps, Braintree for payments, SendGrid for email etc.
    • thereby an Intelligent System in action

So finally what will work for us to run an AI system is a combination of the above which not just a system but a very intelligent system based on new and enhanced learning.

Figure: Intelligent System working (Source: Internet)

Just to illustrate this in a live example for the most Successful Internet media-service provider and Production Company in North America. I have put together the to-be story of a system of intelligence that they built.

There is a big difference between merely having all the necessary ingredients of the new machines and actually getting them to perform at a high level. An intelligent system that can help you be the Michael Phelps of whatever race you are in will have all or most of the below characteristics to make it successful.

  • Learn more than any other system
  • Open to more changes/corrections
  • Not just being automated but also involve human inputs
  • Focused on a confined problem
  • Individual experience has to be top priority
  • Look out for Constantly improving system

Once the intelligent system is in place, you finally need a way to measure whether it is doing the right things or not –

  1. An Intelligent has to become better and better and that depends solely on the ‘Quality of Data’ that is being fed into it
  2. Intelligent systems has to be a journey in an organization and not just an individual contribution
  3. System should take ownership of more and more data analysis and should reduce human intervention

Every day that passes gives us more evidence and strengthens our conviction that the intelligent systems that we are trying to understand in this part are the engines of the fourth industrial revolution. Individuals and companies that are early birds on this bandwagon are the ones that are reaping rich benefits out of solving their major problems. So it’s but obvious that we need to the AI/ML way sooner or later. Are you ready?

Stay tuned…. Part III of this foray, we will quickly look through the making of Data that makes an AI system successful and then dwell upon the Digital Business Models and Solutions: – “Robots” that outline the design and delivery of the AI platform.

Please feel free to review my other series of posts 

Authored by Venugopala Krishna Kotipalli

AI / ML – Past, Present & Future – Part I

The World has seen development/growth primarily driven by the Industrial revolutions. Each of these revolutions changed the way we looked at a time of economic dislocation; when old ways of production become defunct and they had to give way to far better/newer ways of production that could harness the improvement brought in by new machines. The First Industrial revolution was powered by the invention of the loom the second by the steam engine and the third by the assembly line, the fourth however will be powered by the machines that seem to think. We are HERE in the fourth one.

Between each industrial revolution to the next, there is long and bumpy road connecting one era of business and technology to the next, the evolution of each industrial revolution follows the part of an S-curve (as show in figure below)

  1. IDEA BURST : Breakthrough, high concentration in wealth, new industries created, new tech create press clipping but no impact on existing industries
  2. BUMPY ROAD :Revolution stalls, skepticism on value creation in phase I, economic models and value chains created, change in existing industries
  3. MASSIVE LIFT UP : Everybody richly rewarded, National GDP gets vertical lift off, Large wealth distribution

Just to see the impact of such AI driven world that is in front of us, few commonsensical usage of AI in the future world are below.

  • 1/3rd of all food produced go to waste, could be moved to Third world countries by usage of AI to address the hunger prevalent there
  • 12 million Medical misdiagnoses in US only contribute to 4,00,000 deaths. By the right usage of AI, most of these deaths can be avoided
  • Driverless cars are reducing the Annual # of accidents from 4.2 to 3.2 per million miles driven. This will improve as days go by

Now that the machines are in, we need to see what is that we are supposed to expect

  • Technology will be embedded into everything (IoT – Internet of Things)
  • As machines become better, it is but obvious that by year 2030 standards, the current frame work of machines will stink. Advent of improvements on these machines
  • Becoming Digital – mastering the three Ms (raw Materials, new Machines, and business models)

While the above statistics on Job displacement is detrimental to many of human futures, however the pace of elimination will be slow. Consider the following

  • Most likely scenario : 12% elimination in next 10 to 15 years
    • 3 scenarios
      • Job Automation: 12% are at risk
      • Job Enhancement: 75% of existing jobs will be altered
      • Job Creation: 13% net new jobs will get created due to new machine requirements or new job categories

The advent of 13% of new job and the ones that cant automated and enhanced still would need human intervention and keep the true need of humans in place vis-a-vis machines replacing each one of us – Scary isnt it?

Source: internet

So Let me introduce you to some key definitions to keep us on track of this arduous journey

What is AI?

Artificial Intelligence – Coined in 1956 by Dartmouth Assistant Professor John McCarthy, ‘Artificial Intelligence’ (AI) is a general term that refers to hardware or software that exhibits behavour which appears intelligent. In the words of Professor McCarthy, it is “the science and engineering of making intelligent machines, especially intelligent computer programs.”

Other sources terms AI(Artificial Intelligence) as an area of computer science that focuses on machines that learn. There are 3 types of AI prevalent

  • Narrow AI (ANI)/Applied AI: Purpose built and business focus on a specific task. E.g. Driving a car, Reviewing an X-ray, Tracking financial trades
  • General AI (AGI)/Strong AI: pursuit of a machine that has same general human intelligence as a human. E.g. figuring out how to make coffee in an average American home
  • Super AI: 10(or 1000) steps ahead of us. Technical genie – havoc around us

By the way, AI has existed for decades, via rules-based programs that deliver rudimentary displays of ‘intelligence’ in specific contexts. Progress, however, has been limited — because algorithms to tackle many real-world problems are too complex for people to program by hand. To resolve the area of complex problems is the world of ML (Machine Learning)

Machine learning (ML) is a sub-set of AI. All machine learning is AI, but not all AI is machine learning . Machine learning lets us tackle problems that are too complex for humans to solve by shifting some of the burden to the algorithm. As AI pioneer Arthur Samuel wrote in 1959, machine learning is the ‘field of study that gives computers the ability to learn without being explicitly programmed.’

The goal of most machine learning is to develop a prediction engine for a particular use case. An algorithm will receive information about a domain (say, the films a person has watched in the past) and weigh the inputs to make a useful prediction (the probability of the person enjoying a different film in the future).  Machine learning algorithms learn through training. An algorithm initially receives examples whose outputs are known, notes the difference between its predictions and the correct outputs, and tunes the weightings of the inputs to improve the accuracy of its predictions until they are optimized.

Why is AI important?

AI is important because it tackles difficult problems in a way our human brain would have done but much faster and less erroneous- obviously resulting in human well-being. Snce the 1950s, AI research has focused on five fields of enquiry:

  1. Reasoning: the ability to solve problems through logical deduction
  2. Knowledge: the ability to represent knowledge about the world (the understanding that there are certain entities, events and situations in the world; those elements have properties; and those elements can be categorised.)
  3. Planning: the ability to set and achieve goals (there is a specific future state of the world that is desirable, and sequences of actions can be undertaken that will effect progress towards it)
  4. Communication: the ability to understand written and spoken language.
  5. Perception: the ability to deduce things about the world from visual images, sounds and other sensory inputs.

AI has thus already gone past imaginations and already is part of our home, workplace, community and what not. To say it simply, it’s infiltrating all the frameworks that are driving the global economy. From Siri, Alexa, Google Home, to Nest to Uber the world is covered with smart machines which are operating on extremely strong software platforms which in turn are in self learning mode. And I am not sure if it’s the best part or the scary part – This is the just the BEGINNING!!!. I call it scary because these new inventions are always “ready to learn” and constantly “adding intelligence” which will very soon challenge and enhance the intellect and experience of the savviest professionals in every sector.

Stay tuned…. Part II of this foray, we will dwell upon the Raw Materials and New Machines that outline the core of the AI platform.

Please feel free to review my other series of posts 

Authored by Venugopala Krishna Kotipalli