Artificial Intelligence: Everything You Want to Know
By the end of this 10-minute read, you will hopefully have a comprehensive overview of Artificial Intelligence (AI). What is Artificial Intelligence? We’ll try our best to give you straightforward and relatable answers in this quite heavy subject. After defining AI and its subfields, we will have a look into the brief history, current use cases, most common fears, and mind-boggling predictions for the future. We encourage you to dig deeper into the 10 great resources we have listed for you at the end of this article.
ARTIFICIAL INTELLIGENCE HAS BECOME THE NEW BUZZWORD leaving IoT, Big Data, Automation, Augmented Reality and Virtual Reality in shade. Many people believe this is all just hype. The reality is, the different subfields of AI are already being used in medicine, the automotive industry, quantum physics, the financial world, manufacturing and different types of business software.
The generalised definition of Artificial Intelligence is – a computer program which can carry out a complete simulation of the human brain. While this is not wrong and creating a self-teaching AI that will mirror human learning might be the ultimate goal for some researchers, it’s not the whole truth. The scope of Artificial Intelligence is much broader, including technologies like Virtual Agents, Natural Language Processing, Machine Learning Platforms and many other.
To give you more context, have a look at Forrester’s TechRadar report on Artificial Intelligence, a detailed analysis of 13 technologies companies should consider adopting. Gil Press, a Forbes contributor, has picked out the top 10 AI Technologies:
- Natural Language Generation: Producing text from computer data.
- Speech Recognition: Transcribe and transform human speech into a format useful for computer applications.
- Virtual Agents: From simple chatbots to advanced systems that can network with humans.
- Machine Learning Platforms: Providing algorithms, APIs, development and training toolkits, data, as well as computing power to design, train, and deploy models into applications, processes, and other machines.
- AI-optimised Hardware: Graphics processing units (GPU) and appliances specifically designed and architected to efficiently run AI-oriented computational jobs.
- Decision Management: Engines that insert rules and logic into AI systems and used for initial setup/training and ongoing maintenance and tuning.
- Deep Learning Platforms: A particular type of machine learning consisting of artificial neural networks with multiple abstraction layers.
- Biometrics: Enable more natural interactions between humans and machines, including but not limited to image and touch recognition, speech, and body language.
- Robotic Process Automation: Using scripts and other methods to automate human action to support efficient business processes.
- Text Analytics and NLP: Natural language processing (NLP) uses and supports text analytics by facilitating the understanding of sentence structure and meaning, sentiment, and intent through statistical and machine learning methods.
On a more relatable note, you might have encountered AI in your everyday life talking to virtual assistants like Siri or Alexa, using Waze or Google Maps to avoid traffic, sharing a ride with apps like Uber or Lyft, flying on a plane that uses an AI autopilot. AI technologies are even used in Social Media – face recognition, movement tracking to apply animated filters, news feed personalisation, content creation. You probably have also read about the development of self-driving cars and human-like robots. These are only a few examples which show that AI can mean a broad study on developing a superintelligence and on the same time – implementing a small enhancement we use in our everyday lives or businesses.
Artificial Intelligence versus Machine Learning
The terms “artificial intelligence” and “machine learning” are sometimes used interchangeably due to the recent focus on imitating the human thought process. Machine learning has become an integral part of the contemporary understanding of AI, but not to confuse – “artificial intelligence” is a wider term, while “machine learning” is its subfield.
The principle of machine learning is that rather than have to be taught to do everything step by step, machines can learn to work and improve by observing, classifying and failing, just like humans do.
Another way to define and differentiate AI are the Three Stages of AI – Machine Learning, Machine Intelligence and Machine Consciousness. Or according to UBS – Artificial Narrow Intelligence, Artificial General Intelligence and Artificial Super Intelligence. The first stage is limited to only one functional area, in the second stage AI should be able to combine different narrow areas to perform tasks on a human skill level. The final stage is an intelligence, which surpasses human capabilities. Currently, we are in the transition from the first to the second stage.
There are numerous ideas and events which refer to the development of AI from the past century.
- The first mention of the word “robot” takes us back to 1921 when Czech writer Karel Čapek uses the world in his play “Rossum’s Universal Robots”. Fun fact: the word “robot” comes from the word “robota” (work).
- One of the most significant influencers has been Alan Turing, who in 1951 published an article “Computing Machinery and Intelligence” in which he proposed the “imitation game”. It has become known as the “Turing Test”.
- The official birth of AI as a science took place in 1956 at the Dartmouth Summer Research Project on Artificial Intelligence. John McCarthy, inventor of the programming language LISP, coined the term “artificial intelligence”. The initial goal was to research how machines could simulate aspects of intelligence.
- In the 1960s and 1970s, AI researchers began to use computers to recognise images, translate between languages, and understand instructions in normal language. AI subfields emerged, which has enabled deep technical progress along different fronts.
- A significant achievement in the year of 2016 was Google’s development of AI named AlphaGo which could beat expert players at the complicated board game Go. This is an important step in machine learning because AlphaGo figured out how to play the game on an expert level by itself.
To clarify the current situation once more – AI as a superintelligence does not yet exist. But AI as a science which has several achievements in its subfields is affecting our lives already today. There are many predictions made for the future, but what will eventually happen – even the world’s leading researchers can’t say for sure.
If you have been thinking that there is still time to get onboard and AI is just a hype, think again. Here are some hard facts about what’s happening in the field of AI already today:
- According to Cowen and Company Multi-Sector Equity Research study, Artificial Intelligence: Entering A Golden Age For Data Science – Digital Marketing & Marketing Automation, Salesforce Automation (CRM) and Data Analytics are currently the top three areas ripe for AI/ML adoption.
- The same study found that 81% of IT leaders are currently investing in or planning to invest in Artificial Intelligence. 43% of those leaders are evaluating and doing a Proof of Concept, and 38% are already live and planning to invest more.
- Market forecasts vary, but all consistently predict explosive growth. IDC predicts that the AI market (including hardware & services) will grow from $8B in 2016 to $47B in 2020. This forecast includes $18B in software applications, $5B in software platforms, and $24B in services and hardware. Accenture adds to this – the AI market is growing exponentially, reaching $400B in spending by 2020.
- Accenture states that the total number of AI start-ups has increased 20-fold since 2011. The top verticals include FinTech, Healthcare, Transportation and Retail/e-Commerce. According to angel.co, there are 2,200+ Artificial Intelligence start-ups, and well over 50% have emerged in just the last two years.
- Cowen predicts that an Intelligent App Stack will gain rapid adoption in enterprises as IT departments shift from system-of-record to system-of-intelligence apps and platforms. The future of enterprise software is being defined by increasingly intelligent applications today, and this will only accelerate in the future.
To go into more detail, let’s look at some examples how companies are leveraging AI technologies.
General Electric (GE), a manufacturing industry company with a 120-year history, is taking a big leap from being seen as “traditional” to become a “data-driven” company.
Jeff Immelt, the CEO of GE likes to say that “If you woke up as an industrial company today, you will wake up as a software and analytics company tomorrow. It doesn’t mean that you’ll be selling software, rather than software will be an important part of whatever you’re making.”
The main focus in GE is on making machines smarter, leveraging machine learning to create “digital twins” – a digital replica, or data-based representation of an industrial machine. This model can then be used to diagnose faults and predict the need for maintenance, ultimately reducing or eliminating unplanned downtime in that machine.
Starbucks is also not waiting to embrace the benefits of AI to improve their Customer Experience. They are currently testing the opportunity to use Amazon’s virtual assistant Alexa for placing voice-activated orders. This is again an example of a big company, but as it becomes a normality for large enterprises, customers will expect the same level of service from their local businesses.
For smaller businesses, the first contact with AI is probably through using AI business applications and software, rather than implementing AI technologies to their own product or service. Unfortunately, SalesForce’s Connected Small Business Report notes that only 21% of small businesses are currently using business intelligence and analytics.
Tony Rodoni, the VP for the Commercial Business Unit at SalesForce explains that “AI has the potential to make every company and every employee smarter, faster, more operationally efficient and more productive. For small businesses with limited time and resources, the ability to work smarter and automate basic tasks can be a life-saver.”
Read on: How AI is Changing the Way We Work
FEARS AND OBSTACLES
According to a survey Forrester conducted last year, 39% of businesses have not found a clear purpose for implementing AI technologies and 33% do not have the skills required to do so. Other objections for using AI technologies include not having a budget for it or not believing in the importance and reliability of AI systems.
Being human means having rational and irrational fears. It is only logical to be cautious towards everything we do not know or fully understand. As AI is such a wide field of study, there is an equal amount of risks and potential benefits surrounding this topic. World’s top technology leaders Stephen Hawking and Elon Musk are on the sceptical side of this debate, while Microsoft, Apple, Google and many others are already eagerly taking advantage of the AI technology.
In my opinion, technology can only become dangerous to the human kind, when it is developed and used by people who do not have the proper capabilities or the best interests in mind. Or, as Elon Musk has said, “a scientist will get so engrossed in their work that they don’t really realise the ramifications of what they’re doing.”
We must be cautious towards the ultimate outcome of developing intelligence on the same level or higher than humans, but we can not turn away from the many benefits various AI technologies are providing us today and in the future.
IMPORTANCE AND BENEFITS
If developing a higher intelligence than the human brain holds such dangerous risks as the end of the whole human race – why are we doing this? Even though Stephen Hawking has been talking about the dangers of AI, he has also pointed out the positives AI research could bring, saying “the potential benefits of creating intelligence are huge.”
- AI can help boost workforce productivity by reducing the time spent on organising work, communication and preparing for meetings, keeping remote workers more engaged and reducing distractions.
- In the opposite opinion that AI and robots will rob us from our jobs, they can actually make our jobs easier and create new more meaningful and fulfilling careers. Remember the shift from working in agriculture to manufacturing to other urban jobs.
- Even if technologies are getting more complex, AI assistants can actually help to simplify the concepts to be more comprehensible for everyone. For example, assist non-tech-savvy people to complete tasks online, analyse data faster, help businesses allocate their online marketing budgets and find the best channels, etc.
- Improve our health and well-being. Using AI in healthcare we can draw more accurate and faster diagnosis, track our health issues, accelerate research and develop new treatments and vaccines.
- Increase auto safety and decrease traffic. 95% of automobile accidents happen due to human error. Eliminating the human factor can significantly decrease the number of accidents.
- In addition to daily and local changes, raising efficiency in business management and productivity, there are global changes, which can transform the world, society and environment into something we can’t even dream of.
Stephen Hawking comments, “We cannot predict what we might achieve when our own minds are amplified by AI. Perhaps with the tools of this new technological revolution, we will be able to undo some of the damage done to the natural world by the last one – industrialisation. And surely we will aim to finally eradicate disease and poverty.”
As some are afraid of crossing the line with the achievements in AI technologies, some are fanatical about the ways AI could change our lives and the society as we know it. Ray Kurzweil is one of the biggest thought leaders in the subject of AI and future technologies. In his many books and presentations, he has described numerous technological advancements, which have come true so far. According to Bill Gates, Kurzweil is “the best person to be predicting the future of artificial intelligence.”
Here are some of the mind-boggling predictions by Ray Kurzweil:
- By the 2020s, most diseases will go away as nanobots become smarter than current medical technology. Self-driving cars will become the normality.
- By the 2030s, virtual reality will begin to feel 100% real. We will be able to upload our mind/consciousness by the end of the decade. This can theoretically mean reaching immortality – upload our consciousness into an immortal digital form and reach the Singularity.
- By the 2040s, nonbiological intelligence will be a billion times more capable than biological intelligence (a.k.a. the human race)
- By 2045, we will multiply our intelligence a billion-fold by linking wirelessly from our neocortex to a synthetic neocortex in the cloud.
Additionally, Elon Musk has commented that the most positive outcome could be “having some sort of merger of biological intelligence and machine intelligence.” So, both sides – eagerly optimistic and cautiously sceptic are acknowledging that the technological evolution is happening, and we must not be ignorant. Why is it so important to look into the future? Why is it important to prepare? Why not just go with the flow?
Because, as humans, we are biased to think linearly, but the technological growth is exponential. Are you ready to grasp the thought of your company keeping up with the pace of increasing computing power as it doubles in every two years?
Even if these predictions seem outrageous and futuristic today, the key takeaways from this are – the world is changing faster and faster, and we all are forced to step out of our comfort zones to keep up with this pace. Or better – benefit from or monetize these changes. Do you want to be the disruptor, the early adopter, the mediocre majority or the one lagging behind?
YOUR ACTIONS TODAY
Okay, we now comprehend to some extent the meaning, subfields and current use cases of AI, the short history behind it and the fears that come along with it. What does it all mean for your business? What to do with this information in the present day?
- Do not lose sight. Keep your eye on the emerging trends, because trends can turn into everyday normalities. Read the resources we have listed for you below. Create your own list of resources, which you think are most relevant to your company and industry. There are so many opportunities when it comes to AI – set out your own business-specific goals and find focus.
- Managing your business versus improving your product. Depending on your interest, you can go in depth into every subfield, but especially crucial is to take an interest in AI technologies which can help you manage and grow your business or improve your product or service. You might not be keen on building your own Superintelligent Virtual Assistant, but you probably want to use business software which can help you reduce costs or sell your product at a higher price.
- Do not disregard updates. Keep up with the new features and improvements your current software providers are making. This is a small step you can take, but it is the very least you should do. If you are eager to give some input yourself – share your feedback and thoughts on how to improve the software and technology you are currently using.
- Do not underestimate the importance and growth of technology. As we know, the speed at which technology is evolving is exponential. We all have to step up our game by learning and adapting faster. The common misconception is that you need to have a degree in a technological field to understand the subject – the truth is, you don’t. Understanding how to leverage technology has become an integral part of every industry and occupation. There are so many materials and courses on the Internet – just read.
- Cultivate a learning culture in your company. Modern Human Resource managers have deemed it nearly impossible to create strict development and training programmes for knowledge workers because the data expires too fast. Focus on incentivising and guiding your team’s growth to be flexible and self-initiated.
- Give AI a try in your personal life. Before implementing new technologies in your company, you might want to try them on a personal level. So, when your company becomes ready for deploying AI, you will already have the upper hand. Testing out Amazon Alexa or Google Home yourself might give you a new perspective for brainstorming how to leverage AI in your business.
It is a challenge for every company to be up-to-date in this ever-evolving digital era, but it is a necessity. To give you further insight, here are some great resources about Artificial Intelligence:
- “The Singularity is Near: When Humans Transcend Biology” by Ray Kurzweil. Kurzweil explains his law of accelerating returns which predicts an exponential increase in technology. He describes his theory of Singularity, which represents the future of society, where “machines will be human”.
- “How to Create a Mind: The Secret of Human Thought Revealed” by Ray Kurzweil. In it Kurzweil describes his Pattern Recognition Theory of Mind, the theory that the neocortex is a hierarchical system of pattern recognisers and mapping the human mind will lead to the development of superintelligence.
- “Artificial Intelligence: A Modern Approach” Third Edition by Peter Norvig and Stuart J. Russell. This textbook will give you a year’s worth of learning and diving into different subtopics.
- “Superintelligence” by Nick Bostrom. A discussion over developing a superintelligence – “potential upside is clearly enormous, but the downside includes existential risk.”
- “The Master Algorithm” by Pedro Domingos. A great introduction to machine learning, and how it connects with our everyday life.
- Numerous courses about AI on Coursera and Udemy.
- Feedspot has listed top 50 blogs and websites for an AI enthusiast.
- “Benefits and Risks of Artificial Intelligence” by Max Tegmark. An overview of various myths and controversies about AI. There is also a great list of recommended references (videos, articles, books, research, etc).
- “My Curated List of AI and Machine Learning Resources from Around the Web” by Robbie Allen. A helpful guide through the endless learning opportunities around the Web.
- “A list of artificial intelligence tools you can use today” by Liam Hänel. AI tools divided into different lists for personal and business use.
Artificial Intelligence in general means creating an intelligence which is on the same or higher level of capacity than humans. Developing a Super Intelligence would be the final stage of AI. Currently, we are transitioning from the first stage of Machine Learning into the second stage of Machine Intelligence. The research in AI and its subfields has provided us numerous benefits, tools and knowledge we can leverage already today.
Industry leaders like General Electric, Starbucks, Google and Microsoft are eagerly taking advantage of the small advancements which have been done so far, and investing tremendous resources into research and development. Soon, this will become a normality and catch up with smaller local companies.
The key takeaway from all this overwhelming (yet overly exciting) information is – the tech is ours to program and use for our benefit. Letting fears or ignorance control our judgement would be a mistake. To keep up with the exponential growth of technology, take small steps and push yourself to learn more and adapt faster. Regardless of Super Intelligence being ultimately developed or not, be in control of your life and business by having a comprehensive understanding of technology.
As always, we’d love to hear your thoughts on using Artificial Intelligence in your business. Leave your comments below!