AI News, AI Companies & Cybersecurity:The Race To Build Artificial ... artificial intelligence

Benedict Evans

Since the foundation of machine learning is data - lots and lots of data - it’s quite common to hear that the concern that companies that already have lots of data will get even stronger.

The trouble was that though this works in theory, in practice it’s rather like trying to make a mechanical horse - it’s theoretically possible, but the decree of complexity required is impractical.

ML replaces hand-written logical steps with automatically determined patterns in data, and works much better for a very broad class of question - the easy demos are in computer vision, language and speech, but the use cases are much broader.

It is easy to imagine virtuous circles strengthening a winner: ‘more data = more accurate model = better product = more users = more data’.

From here it’s an easy step to statements like ‘Google / Facebook / Amazon have all the data‘ or indeed ‘China has all the data’ - the fear that the strongest tech companies will get stronger, as will countries with large populations and ‘permissive’ attitudes to centralised use of data.

This is much the same as all previous waves of automation: just as a washing machine can only wash clothes and not wash dishes or cook a meal, and a chess program cannot do your taxes, a machine learning translation system cannot recognise cats.

Both the applications you build and the data sets you need are very specific to the task that you’re trying to solve (though again, this is a moving target and there is research to try to make learning more transferable across different data sets).

To come at this from the other end, if you’re creating a company to deploy ML to solve a real-world problem, there are two basic data questions: how do you get your first data to train your models to get your first customer, and how much data do you actually need?

Of course, the second question breaks down into lots of questions: is the problem solved with a relatively small amount of data that you can get fairly easily (but many competitors can get), or do you need far more, hard-to-get data, and if so is there a network effect to benefit from, and so a winner takes all dynamic?Does the product get better with more data indefinitely, or is there an S curve?

Machine learning means they will be able to do sentiment analysis on a million emails (‘show me anxious emails’), without needing to train that model on the data from your case, because the examples of sentiment to train that model don’t need to come from this particular lawsuit (or any lawsuit).

Drishti, another portfolio company, uses computer vision to instrument and analyse production lines - some of those capabilities are trained on your data and some are not specific to your business at all and work across industries.

World's Top 20 AI Drug Development Companies

As Margaretta Colangelo,Partner at Deep Knowledge Ventures, mentions in her recent article 'Although pharmaceutical companies spend over $172 billion on research and development annually, over 90% of molecules discovered using traditional techniques fail in human clinical trials.

Moreover, 75% of newly approved drugs are unable to cover the cost of development and some analysts predict that ROI in pharmaceutical R&D may hit zero by 2020.' If we accept that this prediction has some good chances to become reality, then we should see the AI as the Deus ex Machina in the field of pharmaceutical R&D to keep a business alive, but also give the invaluable hope of choice to some patients.

According to DKA 'The barriers to entry in the AI Healthcare industry are lower than for AI in drug discovery and these companies can achieve real results with much less funding and fewer highly specialized employees.' AI for Drug Discovery companies need much higher levels of expertise in traditional biopharmaceutical science (biochemistry, biology, biomedicine, etc.) and in core AI techniques.

How Will Tech Companies Make Money From Artificial Intelligence?

AI is slated to add $15.7 trillion to global gross domestic product (GDP) by 2030, according to research by PwC, and it's going to change how we use transportation, and even how we do our jobs.  However, AI's potential doesn't mean much to investors unless they understand how companies are using and benefiting from this technology.

The company's Google Home smart speaker devices field similar questions you'd type into Google's search engine, but instead, it uses the company's AI assistant, called Google Assistant, to listen to requests.

Amazon's Alexa AI assistant makes it easy for users to repurchase items they've bought in the past, or to quickly add an item to their cart right when they're thinking about it, via a voice command to Alexa.

Speaking about AI and AWS back in 2017, Amazon CEO Jeff Bezos said, '[I]nside AWS, we're excited to lower the costs and barriers to machine learning and AI so organizations of all sizes can take advantage of these advanced techniques.'

NVIDIA believes AI demand will continue to grow, and that the need for more chips in this space will create a total addressable data center market of $50 billion by 2023.

Last, but not least, artificial intelligence is allowing cars to learn how to drive on their own, which could bring about a $7 trillion passenger economy over the next three decades.

While Waymo One is just getting started, the company's onboard AI system has already driven more than 10 million miles on its own and has been through 7 billion miles of virtual testing.

But the companies above have been heavily investing in AI and machine learning for years, and the products and services listed above are tangible evidence that these tech giants can -- and already are -- making money from AI.

The great news is that this market is just getting off the ground right now, and with Wall Street feeling a bit apprehensive about tech stocks at the moment, many of these AI tech giants are currently trading at a discount.

AI In 2019 According To Recent Surveys And Analysts' Predictions

Artificial Intelligence (AI) is the talk of the world and it features prominently in predictions for 2019 (see here and here) and recent surveys by consulting firms and other observers of the tech scene.

Here are the key findings: Consumer adoption: “Smart speakers” lead the way to the AI-infused home of the future Smart speakers (e.g., Amazon Echo and Google Home) will become the fastest-growing connected device category in history, with an installed base projected to surpass 250 million units by the end of 2019.

With sales of 164 million units at an average selling price of $43 per unit, total smart speakers’ revenues will reach $7 billion, up 63% from 2018.

(Deloitte) Enterprise adoption: Timid first steps 47% of business executives say their companies have embedded at least one AI capability in their business processes and just 21% say their organizations have embedded AI in several parts of the business.

By 2020, the penetration rate of enterprise software with AI built in, and cloud-based AI development services, will reach an estimated 87 and 83 percent respectively.

24% said their enterprise-wide AI efforts were being led by an AI “center of excellence.” (PwC) 58% of business say less than one-tenth of their companies’ digital budgets goes toward AI and 71% expect AI investments will increase in the coming years.

(PwC) The three most popular uses for AI and machine learning are to increase efficiencies or worker productivity (51%), to inform future business decisions (41%) and to streamline processes (39%).

(McKinsey) While 88% of senior leaders polled agree machine learning or AI help their businesses to be more competitive, only 56% of organizations are currently utilizing these emerging technologies.

69% of respondents believe that machine learning/AI technologies are having a positive impact within theirindustry and 39% believe their organizations are getting the most value out of AI and machine learning.

There is no doubt that AI skills are on the rise, but some typically human skills that today cannot be replicated by machines have been growing almost as fast and are here to stay.

(LinkedIn) While ML is the largest skill cited as a requirement, deep learning (DL) is growing at the fastest rate from 2015 to 2017 the number of job openings requiring DL increased 35x.

AI Hub Tampere helps companies to take the first steps in AI

The new hub focuses on intelligent machines and aims to make artificial intelligence easy to reach for the local companies.

AI Hub Tampere helps companies to understand both the potential and the limitations of AI, giving them a better basis to start buying AI that serves them well.

We are targeting engineers who work in the local companies and know their grassroots-level needs, and also managers who make the AI outsourcing decisions, Huttunen says.

This will happen for example in workshops where engineers from different companies work together on challenges related to AI, and strenghten the whole AI ecosystem at the same time.

The hub is an important part of the AI and analytics theme of Smart Tampere program and helps achieve the strategical and industrial policy objectives of the City of Tampere.

Further to that, AI Hub Tampere can be a concrete asset in the invest-in work, making the region known for its AI expertise in a similar way that Tampere Imaging Ecosystem already does in its field, says Siipola.

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