Paul Wallbank – Brilliant Communications

Tag: artificial intelligence

  • Artificial intelligence and small business

    Artificial intelligence and small business

    How can small businesses use Artificial Intelligence? On Flying Solo, Rob Gerrish and I discuss the various ways AI is going to affect smaller enterprises.

    One of the important things about the discussion is how AI is going to change a range of industries and jobs. The effect on small businesses over the next twenty years will be as great at the Personal Computer was.

    The big takeaway I have for business owners is to actively think about how AI and automation are going to affect their industries, customers and individual companies.

    Have a listen.

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  • When artificial intelligence becomes pervasive

    Once upon a time computers were unusual, getting time on one was only for select employees of large corporations and scientists. Famously IBM’s Tom Watson forecast there would only be a need for five computers, although it seems he never said that.

    Today we’re surrounded by computers in everything from our cars and phones to our teapots and razors and now we’re considering how those devices will affect our future workforce.

    At the core of the discussion about computers and the future of work, is artificial intelligence. What’s notable though is it’s unlikely that AI is going to be an competitive advantage for technology vendors as the functions become built in.

    This is already being seen with Microsoft building AI into its databases and increasingly the intelligence is going to built into the chips themselves.

    In our recent interview with Xero founder Rod Drury, he flagged how AI is going to drive small business accounting. Drury was speaking at the Sydney AWS summit where the hosting company was showing off many of its AI driven services.

    While artificial intelligence is going to be embedded and almost invisible to the user, it is going to be important. A good example is Google’s struggle to maintain quality and honesty in its local search results, a process that is beyond the company’s resources if done manually.

    For the software vendors, the quality of their AI features is going to be one of their key selling points. This is why AWS, Amazon and almost company in the industry is announcing their own initiatives. Google itself should be one of the leaders in this field.

    As automation becomes increasingly taken for granted, artificial intelligence is going to be seen as a fundamental, and invisible, part of computing.

    While AI is going to be essential for the technology vendors, for users we won’t notice it as long as it works properly.

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  • Building the artificially intelligent business

    Building the artificially intelligent business

    It’s been another big year for Xero after the company passed its million user milestone, at the recent AWS Summit in Sydney founder Rod Drury to spoke to Decoding the New Economy about what’s next for the company and for small businesses.

    For a company founded a decade ago, having a million paying customers is a substantial milestone and one Drury seems quite bemused by.

    “It hasn’t really sunk in yet. When we did our IPO our promise was a hundred customers and I can remember when it was our first year our target was twelve hundred customers – I think we got to 1300 – so to pass a million is pretty nuts.

    “What we’ve found is the accounting software market is probably one of the key industries where you’ll see the benefits of machine learning and AI. The reason for that is massive amounts of data but a pretty tight and structured taxonomy so we processed 1.2 trillion pieces of data in the last 12 months so the graph of data is huge.”

    Far more modest volumes of data threaten to overwhelm smaller businesses and this is where Drury sees Artificial Intelligence and machine learning as essential for simplifying services and driving user adoption.

    “One of the challenges is that small businesses might be great landscape gardeners or plumbers but they are terrible at actually coding transactions so we’re now seeing that wisdom of the crowd and all that data that we can code better than most normal people can. So the big epiphany was ‘why don’t we get rid of coding?’

    “Effectively all a small business has to do make sure things like the data of the invoice is in the system and we can do the accounting for them and the accountants can check and see what’s going on.”

    This automation of basic accounting tasks, and how these features are now embedded in cloud computing offerings, is changing how businesses – particularly software companies – are operating.

    “You can’t run domestic platforms any more, because every accountant will have customers that are exporting and what we’re seeing now is global platforms connecting together so, for example, HSBC announced its bank feeds and what we’re doing with Stripe and Square.

    All of the accountants need to be coaching the small businesses exporting. That’s what creates jobs.”

    That global focus of business is now changing companies grow, particularly those from smaller or remote economies like Australia and New Zealand.

    “What we’re finding now is the last generation of the late 90s and early 2000s was very much enterprise technology and normally companies would get to a certain point and then a US public company would have to buy them.

    “Now we’re seeing truly global businesses that aren’t selling out quickly they’re actually creating businesses from this part of the world. People don’t have to live in Silicon Valley anymore, they can live in Sydney’s Northern Beaches or Auckland or Wellington and do world class work.

    That remoteness is something that challenges Xero though as the company tries to get traction in the US market which is dominated by Intuit and fragmented across regional and industry lines.

    “As you start off as a company listed in Australia and New Zealand it’s harder as you don’t get the benefit of the density in a smaller market. Now we’ve done enough to get these bank deals, we can now attract executives of the calibre that feels like long term leadership and that’s the benefit of doing the hard yards for a few years.

    We’re past the beach head phase now and now we’re building the long term business. We want to be a big fish in a small pond.”

    Overall Drury sees the cloud, particularly Amazon Web Services, as being one of the great liberators for business as smaller companies follow Xero’s footsteps.

    “This is one of the amazing things AWS have done, they’ve created this flat global playing field.”

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  • Automating out white collar jobs

    Automating out white collar jobs

     

    The statistics continue to come about the challenging future of work with the Harvard Business Review looking at how artificial intelligence is changing the role of knowledge workers and the World Economic Forum reports how Japan is already well down the track of automating many ‘white collar’ roles.

    A couple of decades or so back, the assumption was ‘knowledge work’ represented the future of employment and the thought of management being replaced by computers or robots was unthinkable.

    That hasn’t proved to be so as the low end jobs, which we thought would be taken up by displaced industrial workers were offshored, subject to a ‘race to the bottom’ in pay rates and, now, are increasingly becoming automated.

    While the robots first came for call centre workers, it’s quite likely the next wave of will affect white colour workers reports Dan Tynan in The Guardian who has an overview of some of the likely fates of various occupations.

    A good example of the shift, are lawyers with Tynan citing the company DoNotPay which uses AI to help customers fight traffic infringements as an example of the legal profession being automated out.

    Bad for young lawyers

    This though isn’t new in the legal profession. Over the past twenty years many roles in fields such as property conveyancing and contract drafting have been offshored, so much so that junior lawyer’s payrates and job prospects have collapsed as entry level jobs have dried up.

    How the legal profession has used automation and offshoring is a good indicator of how these tradition industries are evolving, now a senior lawyer can handle more work and the need for juniors and paralegals is reduced. The work stays with the older worker while younger workers need to look elsewhere.

    While Tynan discounts the effects of automation on the construction and health industries, those sectors are similarly being changed. Robot bricklayers, for example, allow older workers to stay in the industry longer and increase productivity.

    The internet of things and artificial intelligence are similarly taking the load of nurses and doctors while making diagnostics faster and easier with major ramifications of these industries.

    Dirty data

    There are weaknesses in a data driven world and this gives us clues to where the future jobs may lie, the Harvard Business Review optimistically notes many roles can “composed of work that can be codified into standard steps and of decisions based on cleanly formatted data,” however obtaining ‘cleanly formatted data’ is a challenge for many organisations and managing exceptions, or ‘dirty data’ feeds, shouldn’t be underestimated.

    Unexpected consequences exist as well, the media industry being a good example. While the demand for content has exploded, the rise of user generated content on social media and the collapse of advertising models has upended publishing, writing and journalism. While artificial intelligence and animation can replace actors and reporters, it hasn’t done so in a major way yet.

    How industry sectors will be affected by automation is something the US Bureau of Labor Statistics looked at in 2010.

    The roles which the US BLS estimates may be less affected by automation may be more affected than we think – how the retail and media industries changed in the twentieth century is instructive where the models at the beginning of the century were upended but by the end of the millennium employment in those sectors was higher than ever.

    The future of work isn’t obvious and the effects of automation bring a range of unforeseen consequence and opportunities – this is why we can’t rest on our laurels and assume our jobs, trades and professions will be untouched by change.

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  • Rethinking artificial intelligence and the smarthome

    Rethinking artificial intelligence and the smarthome

    What happens when the founder and CEO of one of the world’s biggest tech companies decides to create a genuinely smart home? Facebook’s Mark Zuckerberg spend 2016 finding out.

    “My goal was to learn about the state of artificial intelligence — where we’re further along than people realize and where we’re still a long ways off,” Zuckerberg writes in a blog post.

    The immediate problem Zuckerberg faced in creating his home made Jarvis automation system was many household appliances are not network ready and for those that are,  the proliferation of standards makes tying them together difficult.

    For assistants like Jarvis to be able to control everything in homes for more people, we need more devices to be connected and the industry needs to develop common APIs and standards for the devices to talk to each other.

    Having jerry rigged a number of workarounds, including a cannon to fire his favourite t-shirts from the wardrobe and retrofitting a 1950s toaster to make his breakfast, Zuckerberg then faced another problem – the user interface.

    While voice is presumed to be the main way people will control the smart homes of the future, it turns out that text is a much less obtrusive way to communicate with the system.

    One thing that surprised me about my communication with Jarvis is that when I have the choice of either speaking or texting, I text much more than I would have expected. This is for a number of reasons, but mostly it feels less disturbing to people around me. If I’m doing something that relates to them, like playing music for all of us, then speaking feels fine, but most of the time text feels more appropriate. Similarly, when Jarvis communicates with me, I’d much rather receive that over text message than voice. That’s because voice can be disruptive and text gives you more control of when you want to look at it.

    Given the lead companies like Amazon, Microsoft, Google and Apple have over Facebook in voice recognition, it’s easy to dismiss Zuckerberg’s emphasis on text, but his view does feel correct. Having a HAL type voice booming through house isn’t optimal when you have a sleeping partner, children or house guests.

    Zuckerberg’s view also overlooks other control methods, Microsoft and Apple have been doing much in the realm of touch interfaces while wearables offer a range of possibilities for people to communicate with systems.

    The bigger problem Zuckerberg identifies is with Artificial Intelligence itself. At this stage of its development AI struggles to understand context and machine learning is far from mature.

    Another interesting limitation of speech recognition systems — and machine learning systems more generally — is that they are more optimized for specific problems than most people realize. For example, understanding a person talking to a computer is subtly different problem from understanding a person talking to another person.

    Ultimately Zuckerberg concludes that we have a long way to go with Artificial Intelligence and while there’s many things we’re going to be able to do in the near term, the real challenge lies in understanding the learning process itself, not to mention the concept of intelligence.

    In a way, AI is both closer and farther off than we imagine. AI is closer to being able to do more powerful things than most people expect — driving cars, curing diseases, discovering planets, understanding media. Those will each have a great impact on the world, but we’re still figuring out what real intelligence is.

    Perhaps we’re looking at the what intelligence and learning from a human perspective. Maybe we to approach artificial intelligence and machine learning from the computer’s perspective – what does intelligence look like to a machine?

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