AUTOMATING FOR THE PEOPLE
Artificial Intelligence is not necessarily what you think. It’s not what Hollywood has made it up to be. It is not the beginning of the end. We are way beyond that already. It is not the rise of the machines. It is the taming of the machines already here and the ones to come.
AI is also one of the hottest tech topics of 2017, with numerous industry leaders sounding in on potential prizes and pitfalls of embracing synthesized intelligence. Firm warnings have come from esteemed technologists like Elon Musk, Stephen Hawking and Bill Gates: A machine intelligence with the powers to perform intellectual tasks like a human being could bring about the singularity and a following apocalyptic end to human kind, our civilization and the human race as we know it.
Warranted warnings indeed. It is complex, and it has the ability to change the modern world. But it does not have to go awry. Even with industry heavyweights like Google, IBM, Microsoft, Facebook and dozens more investing large resources into the AI possibilities, Strong AI (i.e. the one we should worry about) is still a far cry away. Right now all we see are possibilities.
What this means is that the time is ripe for SMBs, private entities and even large-scale communities to become enlightened and informed. As the AI phenomenon grows, opportunities arise. They come in the form of what is called Applied AI: Practical uses for a new age within data utilization.
That is where we come in. That is why we started the DT AutoThink AI Lab initiative.
We have our work cut out for us, but let’s start by explaining the what before the why and how.
"The business plans of the next 10000 startups are easy to forecast: Take AI and add X"
- Kevin Kelly, WIRED
“I believe that at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted.”
– Alan Turing
What is AI, anyways?
As with many emerging technology phenomena, AI has no real singular, clear definition. So scientists argue. People are confused. Some are scared. But most are intrigued.
Breaking it down to simpler terms is essential.
Simply put artificial intelligence is a sub-field of computer science. Its goal is to enable the development of computers that are able to do things normally done by people. To be more specific: Things that would normally require human intelligence. That rules out assembly line robots and automated production with predefined, repetetive patterns and no on-the-spot machine decision making, but includes things like making hard choices (sometimes ethical), adding experience to new tasks and finding new methods of solving challenges.
In other words AI is a software that:
- Perceives its environment
- Adapts to new information
- Learns from every interaction
- Takes action to achieve its goals
- Optimizes to specific outcomes
Sounds great, right? Yes. But is it easy to get right? Not at all.
To get there we need to enable machines to build and rebuild their own minds.
Which brings us to Machine Learning.
Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. So do we.
What is Machine Learning (ML)?
ML is the foundation of A.I. It provides algorithms and methodologies to learn from inputs, experience and desired outcomes. It creates a model that can be implemented in machine structures to make decisions and take actions.
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. So do we.
Some methods and approaches to Machine Learning include:
- Supervised Learning: Learn from data labeled by desired outcome
- Unsupervised Learning: Learn to cluster & summarize similar inputs
- Deep: Learn through a hierarchy of simple to complex concepts
- Reinforced: Learn by continually interacting with an environment
- Active: Learn by asking questions to increase confidence
- Evolutionary: Learn to optimize using introduced randomness
No easy task, but this is where the tech industry is making real progress these days. What still remains are the questions of how to use it and why. Which brings us to the most tangible aspect of AI in the years to come: Application.
What is Applied AI?
Like mentioned before, there are several levels of AI, from Weak A.I to Strong A.I and everything in between. Strong, Skynet AI-level threats are still years and decades away, so looking at Applied A.I, otherwise known as AAI, Narrow AI or Weak AI, is what will affect businesses and modern life the most in the near future.
AAI does not try to replicate human cognitive abilities, but is much more specialized. Simply put AAI is software that equals or exceeds human effectiveness or efficiency for specific tasks. It’s what Google Search, Amazon’s “We think you’ll like this”-lists and Facebook’s tailored fake news feeds are made of.
It is also our core AI focus for the AutoThink initiative, as understanding and using AAI the right way can make a world of difference for a world that can be surprisingly indifferent.
We believe that applied AI businesses will outperform their markets because AI will enable them to provide superior service levels in terms of delivery, availability, accuracy, convenience, engagement, cost and brand. Moreover, their applied AI leadership will create new business advantages and opportunities.
The same AAI that’s leading to the computerization of enterprise workforces is also the solution to making non-automatable work more accessible to a significantly greater number of people. We will move from consumerism to producer-based economy. An economy of people doing what they love and producing what they like instead of being forced to perform meaningless jobs.
When you call the bank and talk to an automated voice you are probably talking to an AI...just a very annoying one. Our world is full of these limited AI programs which we classify as "weak" or "narrow" or "applied". These programs are far from the sentient, love-seeking, angst-ridden artificial intelligences we see in science fiction, but that's temporary. All these narrow AIs are like the amino acids in the primordial ooze of the Earth.
We're slowly building a library of narrow AI talents that are becoming more impressive. Speech recognition and processing allows computers to convert sounds to text with greater accuracy. Google is using AI to caption millions of videos on YouTube. Likewise, computer vision is improving so that programs like Vitamin d Video can recognize objects, classify them, and understand how they move. Narrow AI isn't just getting better at processing its environment it's also understanding the difference between what a human says and what a human wants.
— Aaron Saenz, Singularity Hub, 10 August 2010.
“Sounds fancy, but what’s in it for me?”
Let’s be honest: At some point machines will outperform us in fields we haven’t even thought of yet. Economically, we see a future where enterprise businesses become increasingly efficient by automating much of their workforce using AAI.
In other words: People will lose jobs.
Numbers crunched (by machines, ironically) by the University of Oxford’s Martin Programme on the Impacts of Future Technology show that almost half of all jobs in the Western world (47%) face automation by computers within the next two decades.
The tsunami of social change that will follow presents an incredible opportunity for those who will help people transition from automatable roles into work that is non-susceptible to computerization. That work will leverage social intelligence, creativity, and advanced skills of perception and manipulation.
In other words: It will affect all of us.
But within that change lies a new technological paradigm: The same AAI that’s leading to the computerization of enterprise workforces is also the solution to making non-automatable work more accessible to a significantly greater number of people. We will move from consumerism to producer-based economy. An economy of people doing what they love and producing what they like instead of being forced to perform meaningless jobs.
In other words: Smart machines can help fuel a better human state.
Where does DT’s AutoThink AI Lab come in?
Our AI Lab is set to accomplish two things: Research and improve technical usage and development of AAI and create a framework to allow acceleration of AI technology to change businesses, lives and communities for the better.
As an evolving, producing and expanding entity, we will:
- Be a source of knowledge, for options and advice
- Be a guide to help anyone looking for ways to improve their lives, businesses, operations or strategies
- Devise and implement best practices for AAI
- Research and conduct systems and software, both standard and custom, for market consumption
In accordance with the core principles of Doublethink, the AutoThink AI Lab is based on open mindedness, human values and changes for the better.
The modern world is changing.
Let’s get ready.
Test AI yourself :
Would you be able to tell if you are talking to a person or a computer. Take the test for yourself: http://www.square-bear.co.uk/mitsuku/turing/