This fascinating infographic gives some insight into what enterprise leaders believe about artificial intelligence, robotics and the future of work.
What I find interesting is that those leaders define AI as a technology that thinks and acts like humans. This is definitely the wrong definition to use when evaluating the near-term impact of AI on the workplace. Clearly this type of AI is not eliminating jobs as it doesn’t exist right now.
Current machine intelligence definitely doesn’t think like a human. However, it will have a profound effect on jobs and human work. It’s not clear that it will create jobs to as great extent as it will eliminate them.
Do you think artificial intelligence will eliminate jobs?
A great question for the philosophers: if a machine or even a piece of software behaves in an intelligent manner, then does it need to be accorded rights (and responsibilities)?
One of my favourite episodes of Star Trek: The Next Generation deals exactly with this issue. In “The Measure of a Man”, Lt. Commander Data (who is an android) is the subject of a judicial enquiry to decide if he (it?) is property of Starfleet or an individual.
In the end, the Judge Advocate General rules that Data is indeed an individual, using the reasoning that she is clearly unable to determine if he is not, and a greater injustice would be served if she ruled that he was property.
Perhaps this will be our best approach. If an AI demonstrates a near-human level of intelligence, then we will have to assume that it is deserving of legal protections.
However, it’s a thorny question and one which is bound to occupy biological minds for a long time to come.
Artificial Neural Networks (ANNs) are inspired by the biological nervous system to model the learning behavior of human brain. One of the most intriguing challenges for computer scientists is to model the human brain and effectively create a super-human intelligence that aids humanity in its course to achieve the next stage in evolution. Recent advancements have shown compelling bias towards neural networks owing to its increased accuracy. Neural networks have been shown to be useful to model many problems ranging from a vertical-based to a generic learning system.
Surprisingly, most of the developers using NNs to solve their daily problems do not go beyond using a NN library in a specific language of their choice. The necessity to understand the basic mathematics that governs this beautiful model remains out of scope. This post would be a start to help you open a NN black box and have a better…
Business Owners! Have you got an Internet Strategy? A Mobile Strategy? A Cloud Strategy? A Social Media Strategy?
That’s a lot of strategies to think about. Now, those responsible for business strategy are also being encouraged to have an Artificial Intelligence Strategy. No doubt there will soon be a consultant with a new job title to help: the Artificial Intelligence Strategist.
It is prudent as a business owner to be aware of what’s happening in the AI field so that if you come across something, you can evaluate it and it’s impact on your business.
As with any new area of technology, there will be lots of different things which will be labelled as AI, but won’t really be. In fact, most of what passes for AI today, isn’t really intelligent at all either.
Take Machine Learning as an example. The term itself is misleading in that it seems to indicate that the machine (more likely the computer) is learning something the way that people do. In fact, that’s not happening at all. Machine learning is mostly very clever algorithms that are adept at picking out patterns in noisy data. These patterns can be used to make predictions about other data, with a high degree of accuracy. So fundamentally, it’s building a mathematical model based on a set of training data.
I’ve deliberately glossed over how difficult this is in practice. A lot of the success of these techniques is due to the intelligence of the people creating the algorithms.
When you see the term “Artificial Intelligence”, it’s also worth reminding yourself that AI today is an umbrella term for a toolbox of algorithms and techniques. Each technique has it’s own advantages and disadvantages, and a domain to which it is suited.
It is most definitely not a brain in a box that can carry out lots of different functions. That type of AI is still in the realm of science fiction and certain sensationalist areas of the popular press.
If you see someone selling you a product or a service with AI in the title, ask what sort of AI they are talking about. What technique does it use? What tasks will it automate? How will it be trained? How robust is it? How accurate is it? Claims of 100% accuracy are a definite red flag.
AI is definitely worth keeping an eye on due to it’s disruptive potential if you are a business owner, or responsible for strategy. Just watch out for the snake oil.
This is a useful update from Venture Scanner on the companies that are commercializing Artificial Intelligence. It also has a description of the various categories of AI that are being developed.
At this time, we are tracking 855 Artificial Intelligence companies across 13 categories, with a combined funding amount of $2.73B. These are companies and categories that involve anything and everything that is Artificial Intelligence. Below you’ll find our AI sector map as well as the categorical breakdown of the sector.
Artificial Intelligence Sector Map
Deep Learning/Machine Learning Applications: Machine learning is the technology of computer algorithms that operate based on its learnings from existing data. Deep learning is a subset of machine learning that focuses on deeply layered neural networks. The following companies utilize deep learning/machine learning technology in a specific way or use-case in their products.
Computer Vision/Image Recognition: Computer vision is the method of processing and analyzing images to understand and produce information from them. Image recognition is the process of scanning images to identify objects and faces. The following companies either build computer vision/image recognition technology or utilize it as the core offering…
Nice idea here by Venture Scanner to break down venture capital investment in Artificial Intelligence companies into the various categories commonly grouped under AI.
Looks like Deep Learning is garnering the lion’s share of funding, which is not really a surprise given the recent (and very public) demonstrations of that technology. I’m surprised to see investment still being made in Recommendation Engines though.
It’s still early days in the commercialisation of Artificial Intelligence and there are a lot more opportunities out there.
The following infographic compares total venture funding in our Artificial Intelligence sector to the number of companies in each category. Which AI categories do you think have the most traction and potential for growth? At Venture Scanner, we are currently tracking over 852 Future of TV companies in 13 categories across 62 countries, with a total of $2.71 Billion in funding. To see the full list of 852 Artificial Intelligence companies, contact us using the form onwww.venturescanner.com.
Venture Scanner enables corporations to research, identify, and connect with the most innovative technologies and companies. We do this through a unique combination of our data, technology, and expert analysts. If you have any questions, reach out to info@venturescanner.com.
On Machine Intelligence 