Since the idea of robots doing jobs that a human can do there has been a widespread fear of what this might mean for the working population in the more advanced economies, where they are more likely to appear in greater numbers first. However, a new report by PricewaterhouseCooper in the UK has brought hope, because it claims that AI will actually create more jobs and compensate for those lost to automation.
The PwC report actually sticks a number on new employment opportunities. It says AI will deliver 7.2 million new jobs in healthcare, science and education by 2037. Of course, one has to balance this against the 7 million jobs lost to automation, but as PwC points out, AI is the winner and will boost economic growth.
It also estimates that around 20% of jobs in the UK will be automated over the next 20 years and that every economic sector will be affected. PwC said: “AI and related technologies such as robotics, drones and driverless vehicles will replace human workers in some areas, but it will also create many additional jobs as productivity and real incomes rise and new and better products are developed.”
AI can boost number of healthcare jobs
Fears among employees have already been raised by the use of robots like Pepper, made by Japanese firm Softbank Robotics. Pepper is already in use in banks, shops and social care, the latter being a major concern for Britain at the moment, as endless reports indicate the system is failing. However, the good news for all those healthcare and social workers is that PwcC claims that AI could make these two sectors amongst the biggest winners and generate one million new jobs, which is 20% more than the existing number of jobs in the sector.
Manufacturing, transport and logistics may lose out
On the other hand, as more driverless vehicles arrive and factories and warehouses become more automated, this employment sector could see a reduction in job opportunities, perhaps as much as 22%, or 400,000. The report also says clerical tasks in the public sector are likely to be replaced by algorithms while in the defence industry humans will increasingly be replaced by drones and other technologies.
Does AI offer hope post-Brexit?
This report may lift some spirits at a moment in British politics where things have never looked more unstable for the UK economy, if only for the reason that the business of exiting the European Union has raised more questions marks about the future of British trade and industry than it has been able to answer. However, if AI can create new jobs for working people and at least match the loss of jobs to automation, there’s a hope that the fallout from whatever the negotiations bring over the next few months will not hurt as much as many in business fear.
You’ve probably heard the phrase ‘deep learning’ bandied about in conversation, or maybe you’ve read about it in a post like this one. It seems like almost every tech conversation happening today somehow touches on the topic of AI, machine learning, or deep learning.
These technologies are coming into their own, and are poised to usher-in massive changes not only to the tech industry, but to every aspect of the global economy and society overall. But…what the heck does all of this mean?
In my position as a VC and as a managing director of scale-up program, I’ve worked with a number of startups that are leveraging deep learning along with other AI technologies. I recently created a white paper to help non AI experts understand the potential of deep learning to transform their business.
You can see the white paper here, but I wanted to highlight some of the ideas presented there while providing additional context about why deep learning is so important.
There are key ingredients that are driving the development of deep learning: computing power, data, and AI as a Service.
AI has been around for a long time, tracing its roots all the way back to the 1950s. But there’s more computing power available today than ever before, and it’s starting to make powerful AI possible in ways that weren’t possible even a few years ago.
A lot of this is due to the computing power available via the cloud, but it can also be traced to the sheer advancements made in silicon. This is also the reason we are seeing all of the major players working on dedicated AI chips to take these advancements even further.
The key advancement that all this processing power has enabled is the ability to process the massive amounts of data needed to make AI… well, intelligent. AI is primarily about data and it’s being generated in staggering quantities. In just the last two years, 90 percent of all the data that has ever existed was created. In the world of AI, data is gold, it’s oil, it’s the valuable good that can’t be replaced.
What’s becoming more available and commoditized, however, are the actual AI algorithms needed to process all that data — a kind of ‘AI-as-a-Service.’
Many tech giants are offering AI algorithms via APIs through their cloud platforms. But no one can replace the data itself and it’s there that the hard work still has to be done. One example of that hard word is the labeling of data, which still has to be done by humans.
The reason is one way for machines to learn, is that they need to be fed accurate data that has been properly labeled and vetted. This is still a time consuming and expensive task, and it’s one of the reasons data is so valuable.
When looking at all three ingredients in total, it’s clear that data stands out as the clear winner in terms of value. AI-as-a-Service is a commodity available to all, computing power is a commodity available to all, but data is a valuable natural resource that emerges organically from technological platforms.
Like countries and natural resources, some platforms are data rich, and others data poor. Those companies that can leverage their data resources wisely stand to reap the greatest benefits of the AI revolution.
Machine learning and deep learning
“So what is this stuff already?” I hear you asking. Machine learning, of which deep learning is a subset, is the process by which machines are made more intelligent. Specifically, it’s a way for machines to learn without human interaction, or guidance. Machines are taught to recognize things, complete tasks, make predictions, and a number of other things.
Deep Learning is what I consider to be the “fanciest” of the different types of machine learning because it relies on neural nets, just like the human brain. Don’t be scared by that term — to understand a neural net just think of layers of functions built upon each other.
For example, imagine a scenario from daily life: you see an object, identify that it’s round, identify that it’s orange, and then identify that it’s a fruit, and therefore an orange. Each of those conclusions required a certain kind of function that recognizes a piece of the overall object and that feed off of the result of other functions.
So why is deep learning so fancy?
Now here comes the really exciting part. Deep learning is best suited for processing huge data sets. It enables several different ways of training machines that are truly exciting.
Most of the time, this is done through what’s called ‘supervised learning,’ which processes data that has already been labeled. However, there are other methods that hold incredible potential:
Unsupervised learning — With this method, you don’t tag the data. Rather you can simply throw in a massive amount of data, and task the system with finding patterns or clusters. This is very valuable in doing things like looking at user data to understand, which individuals are likely to convert into loyal customers.
Reinforcement learning — Here it’s about training the system to achieve goals. The way it’s done is by giving the system a reward if it achieves something, and a penalty if it doesn’t. This method can be used for tasks like optimizing the starting position of an article on a page — a click is a reward for the system, and no click is a penalty.
Generative adversarial networks — This is a neural network architecture that features two AIs competing against each other with one AI trying to generate fake data, and the other to identify fake data.
For example, imagine an algorithm designed to generate fake videos of well-known individuals, such as celebrities or politicians pairing off against a counter algorithm designed to identify those fake videos. While this has the potential to create remarkably intelligent and creative AI, the potential for abuse is real, and the risks shouldn’t be dismissed.
So what’s the bottom line?
As I said at the beginning, it feels like everyone is talking about AI right now. But why? A lot of the buzz is manifested as fear: fear that AI will take away our jobs; fear that AI will do us harm. There is some truth in those fears, especially when it comes to jobs.
AI does have the potential to take away jobs and eliminate some kinds of jobs altogether. Of course, AI will also generate new jobs, including roles that we can’t even imagine yet.
However, there is another, ironic, truth about AI: it will help us become more human. It will free us from the boring, monotonous tasks that are better suited for a machine. Doing that kind of work isn’t what we were made for.
Humans are imaginative, creative creatures. We are at our best when we are inventing new things, imbibed with emotion and beauty — not stuck in rubber stamped, mass produced repetitive tasks.
Of course, other obstacles remain. The data we have today isn’t clean enough, algorithms are still in their early stages, and we still need massive amounts of computing power to drive all of this deep learning. Yet, the potential that deep learning holds to enable real, powerful AI solutions fills with me with excitement.
Already today, AI is being used for things as mundane as making us better marketers to more meaningful applications such as scanning medical images to look for disease and in autonomous cars which will make road travel dramatically safer. It makes me feel like a kid to imagine the future we could usher in, and I hope that after reading this you are feeling that excitement as well.
CyberSecurity powered with Artificial Intelligence can boost transparency levels of Cyber playing field.
Glimpse on Artificial Intelligence & CyberSecurity
It’s easy to describe & define Artificial intelligence compare to what actually it is. Now to put it in one liner “AI is kind of intelligence demonstrated by machines to do the same task done by any human using natural intelligence”. In other words same task performed by Human with Natural Intelligence and Machine with Artificial Intelligence should produce same results. Speed, quality and productivity are the measuring units here.
Cybersecurity is a necessary component of every business in this digital age. It protects internet-connected systems, including hardware, software and data, from cyber attacks. For Cybersecurity in contrast to the natural intelligence displayed by humans; machine needs to check and detect anomalies in cyber data.
In a cybersecurity context, AI is a software that perceives its environment well enough to find events and take action against predefined purpose. It can also learn and build the rules on the go as well; actually that’s the real AI.
AI role in CyberSecurity — Series by AILabPage
AI In Cyber Security: A Balancing Force or Disruptor?
Conclusion is already drawn here (could be my opinion) and debate is over: Artificial Intelligence is the Future for Cybersecurity. Biggest fear of today’s time is the concern that hackers are getting much more smarter. These hackers will use artificial intelligence in cyber attacks that are more advanced and harder to detect.
AI has already proven as both a benefit and a threat on the cybersecurity front. Barclays Africa is beginning to use AI and machine learning to both detect cybersecurity threats and respond to them.
AI ROLE IN CYBERSECURITY — SERIES BY AILABPAGE
If we don’t harness the power of artificial intelligence into cybersecurity then, hackers will eventually do this. Now to look at AI as a disruptor Cyber security has to keep up with technological advancements and use the power of AI to stay relevant.
AI may turn out destroyer of cybersecurity as well. For example people who have success in harnessing the power of artificial intelligence to create some sort of program. Combined with existing tools to figure out quarter of the passwords from a set of more than 43 million profiles is a big break through.
AI is kind of intelligence demonstrated by machines to perform the same task done by any human using natural intelligence.
What can AI do for cybersecurity as a guard and as a terminator?
We do have Artificial Intelligence in our systems and business strategies in one or the other way. Still there is a frightening truth about increasingly common cyber-attacks. These anti-attacks techniques need more learning and strengthening of the algorithms used for better monitoring. Sadly most businesses and the cybersecurity players itself are not ready for better use of AI as on date.
In info-security industry that comes first with leadership roles with best-developed products and excellent professional services, this will be known as the winner. Yet the researchers say the technology may also be used to beat baddies at their own game.
AI role in CyberSecurity — Series by AILabPage
AI shouldn’t be taking away jobs from cybersecurity experts. CSE (CyberSecurity experts) does not need any hard-core skill of AI / DataScience but surly needs to upgrade, work hard to understand and lead. What is needed for CSE to understand only “What AI will do to help them in their job” and how. Security alert needs to be looked at and actioned if needed still may be performed by humans until machine learns. In long run humans should lead the process but shouldn’t do them.
There are organization; which has suffered the cyber attacks, about to suffer and may have suffered but don’t know.
AI in CyberSecurity
Will artificial intelligence take over cyber security?
How the next level of Cybersecurity will become an AI powered data-centric model?
AI and Cybersecurity: Friends or foes?
To get answers for many such questions the only solution is to deploy, keep updating rules and let machine learn and adjust its algorithms at backend (sounds like wait and watch). Time will show how this thing will evolve as what is planned today may not be the goal. Hackers will also use AI. So AI will become team player from both the sides. Which mentor train well ?
For cybersecurity, AI can analyze vast amounts of data, help right systems and software’s to make decision and bring huge reductions in attacks & anomalies in much faster way. Since it can work in 24×7 without rest so humans can’t beat the same. AI will allow automated software testing to find and kill bugs before they ship to avoid any banking opportunities on loopholes.
AI likely to kill the need of complex passwords or any sort of passwords. With artificial intelligence need of passwords could come to an end in next 5 to 10 years. The user identity and access management industry needs to redesign and rethinks the next modus operandi.
AI role in CyberSecurity — Series by AILabPage
Challenges and risks with AI for Cyber Security
The first thing many of us think about when it comes to the future relationship between artificial intelligence and cybersecurity which looks promising any ways. At the same time risk relationship is also at the same level. There are a ton of claims around AI and cybersecurity that don’t quite add up. How artificial intelligenceand machine learningwill impact cybersecurity and the industry around it. Intelligent machines that can learn from experience, allowing them to work and react as a human would. This also means same mistakes can’t be ignored.
Many organizations are already beginning to use AI to bolster cybersecurity and offer more protections against sophisticated hackers. AI adoption by malicious actors are much higher compare to real hero. The current cyber landscape favors malicious actors. Many global information solutions companies have reported major cybersecurity incident affecting 143 million consumers in the US.
The Appeal of AI for Cyber Security has extremely good reasons like automation of operational tasks, developing & delivering predictive capabilities, mitigating human biases behaviour and derive doable intelligence. On the down side AI requires high volumes of high quality data to learn. Data silos and varying formats can affect training. Given dynamic cyber landscape use cases need to stand the test of time and context but most of time it negate value.
Cybersecurity is NOT just an information technology department or people in same department problem or responsibility. It is the job of every employee and even customers on the street.
Different users need different security tools (Customer Prospective)
There is need of automated security assistant that learns the needs of the individual user and helps them to apply security tools. For me as an individual my cyber security needs are data confidentiality, data-loss tolerance limits and recovery costs. On back-end machine needs to learn my usage patterns, technical knowledge and security choices I make.
CyberSecurity with Artificial Intelligence will get smarter at the same time cyber crimes as well. What’s the next stage in cybersecurity? May be a simple An AI-powered, data-centric model with huge processing power to analyse trends / patterns and process any velocity of data to make quick decisions. Data-centric models will be able to get rid of noise (garbage data) and discard it for some other day use. Motivations and applications of AI in cyber security has huge list so we not be able to cover all here. Begin your AI filled CyberSecurity journey now.
Conclusion — Cybersecurity is NOT just an information technology department or people in same department problem or responsibility. It is the job of every employee and even customers of the organisation. As per google search engine identities are being stolen online every 3 seconds 24/7. So what are we doing, how can we protect it. GDPR makes it even more relevant. There are organization; which has suffered the cyber attacks, about to suffer and may have suffered but don’t know. To find better answers on this we need AI techniques to get over this. Understanding the Relationship Between AI or a science that can imitate human beings and Cybersecurity that is essential need for all is the key to success in business today.
We are standing at a moment in history where we are about to witness a third industrial revolution brought about by the fast increasing adoption of digital technology by industry after industry.
We have already seen the Internet deliver a second industrial revolution in the postal and telecoms industries, the publishing world and the music industry to name just a few that are extremely obvious.
We also seen new disruptive business models from companies like Uber and Airbnb. This has been called “innovation through creative disruption” by economist Joseph Schumpeter.
I agree with Matthias Stettler of greenmatch AG, who says in a recent Medium article that although there is a tendency for some to focus on the risks apparently associated with the speed at which the digital world is taking over our lives, there is a lot more that needs to be said about the opportunities it offers.
There is the Internet of Things as well as the Internet of Assets, which Stettler suggests combine the real and virtual worlds. And in this realm, it is the Fintechs that are seeing the opportunity to disrupt the conventional financial services, a sector that hasn’t had a shake-up since the first industrial revolution in the 19th century. In this respect, it seems rational to say that it was due for a change.
One thing that fascinates me about Stettler’s view is his argument that what we are seeing now is another industrial revolution. We know that the first one changed society dramatically with the advent of the steam train, followed by the invention of the telephone and so on.
He argues that we most definitely can call it a third industrial revolution, and says: “Considering that an industrial revolution occurs whenever a new primary energy source starts being used (keyword renewable energies), and that new communication technologies (the internet) emerge and changes in mobility (autonomous driving) are involved: then the answer is yes!”
He also points out that robotics and AI are making rapid progress, plus the new technology is decentralised and has virtually no marginal costs. It will also impact on society in every way. Already we are seeing a move towards one that is less divided by the notions of left wing and right wing: these are being replaced by “liberal-global and national-conservative,” as he puts it.
What personally interests me most is this: “Industrial revolutions are times of tremendous opportunities for founders of new businesses.” This third one will be blockchain-backed I would predict and we will see some interesting new products in the fintech arena in particular.