AI creates jobs for real people

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.

Quick guide to understand the hype around ‘deep learning’

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.

Why now?

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.

Originally posted by https://thenextweb.com/contributors/2018/07/14/quick-guide-to-deep-learning/

Cities on the blockchain

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Is it possible to run an entire city using blockchain technology?

Dubai seems to think so. The business and airline hub of the Middle East has set itself the challenging task of being the “first blockchain-powered government in the world by 2020.”

It might sound outrageous right now, but the concept of ‘smart cities’ running on the blockchain is actually not as outlandish, nor as difficult to achieve as you may think. The question really is; where do we start? There are so many millions of possible uses for blockchain in a city, but there are undoubtedly some bigger areas where it will have the most dramatic effect.

IoT devices

Already a number of cities are using IoT devices to do a number of jobs, like monitoring traffic and air quality. Thos IoT devices can be connected to the blockchain. That also applies to any city system that collects data — it can all go on the blockchain. In fact, by putting it all on the blockchain, it will provide an upgrade to the system, and make the information easier to manage and access. Basically it will get rid of all kinds of inefficiencies where officials, such as the police, have to go through X number of other organisations to get a vital piece of information.

Better public safety

Data sharing can have a positive impact on public safety. The blockchain can provide a secure system for sharing sensitive data. One example is working on preventative measures, such as analyzing crime statistics and planning police patrols around that information. Yes, there are issues to be ironed out regarding citizen’s rights to privacy and how much information a government can track, but people are at least having a conversation about it.

Efficient transport

Public transport is vital in most major cities and they don’t work without it. The blockchain offers a lot of potential here, especially for the way passengers pay for their transport. If commuters have a blockchain wallet on their smartphone, they could pay for any transport pass, loyalty programme, or purchase tickets without a card.

Citizen incentives

If you put the public transport payment system on the blockchain, you can also offer customers some incentives. For example, if a city wants its residents to use transport rather than drive, there is a way to incentivise that. When the smartphone wallet shows a citizen has been using public transport for a specific period of time, it is possible to offer them a ‘free ride’ or a discount on an electricity bill. In a smart city, an incentive should push people toward more ethical and sustainable living choices.

And that is what a smart city should be — sustainable and more habitable with fewer issues and inefficiencies. If Dubai achieves its goal, it will have created a blueprint for others to follow.

UK’s FCA opens up sandbox for more play

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In a week where the British government is losing Cabinet ministers on an almost daily basis as a result of party in fighting over the Brexit negotiations, making the pound sterling plunge in value, the UK’s financial regulator, the Financial Conduct Authority (FCA) has taken a bold step forward in recognising the potential of blockchain-based startups.

The FCA started a regulatory ‘sandbox’ some time ago in 2016 and it has just added its fourth cohort of startups to the process. The FCA received a total of 69 applications to participate in the exploration, and this week it has added 11 of the 29 successfully accepted applicants.

In its announcement regarding Cohort 4, the FCA revealed, “Applications came from a diverse range of firms operating across the financial services sector including in areas such as consumer credit, automated advice and insurance.”

The FCA also said, “We have accepted a number of firms that will be testing propositions relating to cryptoassets. We are keen to explore whether, in a controlled environment, consumer benefits can be delivered while effectively managing the associated risks.”

The startups in Cohort 4

One of the businesses in this cohort is 20/30. This London based financial firm is using the DLT to allow “companies to raise capital in a more efficient and streamlined way,” and it is partnering with the London Stock Exchange and Nivaura. According to the FCA’s press release, 20/30 will be issuing an equity token on the Ethereum blockchain. Capexmove, also in this new cohort is offering a similar service.

Another that stands out is called ‘Chasing Returns’. This startup is described as “Psychology-based risk platform that promotes good money management discipline and improves outcomes for customers that trade Contracts for Difference (CfDs). It acts like a digital coach, encouraging adherence to money management and risk exposure levels.”

While for those people with ID problems, ‘Community First Credit Union’ offers an “Initiative to facilitate creation of an identity token that supports customers who lack traditional forms of ID, in order to assist them in accessing bank account services in the UK.”

The latter perhaps answers the issues that many British immigrants have faced recently, most notably those who arrived from the Caribbean on the ‘Windrush’ and in recent months have found themselves at risk of deportation, because of lack of documentation establishing their British citizenship and right to stay.

The FCA has chosen a fascinating selection of startups for Cohort 4 and indicates its willingness to be open-minded and inclusive when it comes to envisioning a future for blackchain-based businesses. It certainly seems to be making better progress with blockchain than the government is with Brexit.