Archive for the ‘artificial intelligence’ Category

An Update on Search Engine Marketing (Pay Per Click)

July 22nd, 2019 by Heather Maloney

It’s been nearly a year since we blogged about pay-per-click advertising in the search engines (‘PPC’ e.g. Google Ads / Bing Ads). A lot has changed over that short time including a new name and a completely new user interface for Google Ads; this blog is intended as an update for business owners and marketing managers to help you keep abreast of what is possible, and the best way to approach search engine marketing.

search engine marketing changes in 2019

Most of the change is around the use of AI applied to the smart delivery of targeted ads, making the customisation of ads for mobile phones much easier to encourage more advertisers to display their ads on mobile, and additional control by Google to ensure positive customer experience.

Quality Scores
I’ve been involved in search engine optimisation (‘SEO’ – the art of achieving high ranks in the search engines for relevant, popular keyword searches) since the mid 1990s and organic traffic (people finding you through searchable content) has always been the #1 priority, and PPC ads the more costly way to get immediate website traffic while your SEO efforts grow your ranks over time. Naturally, the search engines, who only make money from paid ads, don’t see it that way.

Google changes the appearance of ads on a regular basis, and gives preference to the display of ads, all in the attempt to make money at the same time as delivering valid results for searchers. It is in Google’s best interest to ensure that not only are organic search results highly relevant to the searcher, but also the ads. Google want people to be just as willing to click on an ad as they are willing to click on a ‘normal’ search result. To that end, even if you want to pay over the odds to Google for your ad in relation to particular search terms, you may find that your ad isn’t being displayed. That’s because the destination of the ad – the landing page, or web page where the searcher ends up when they click – is now also assessed by Google in determining who to show your ad to. This is referred to as the ‘quality score’. If your ad is about treating back pain, and takes you to a web page that talks about exercise without reference to back pain for example, Google is likely to give your ad a low quality score and be less inclined to present the ad to searchers, and when your ad is presented the click cost will be higher (ouch).

What this means is that you must have high quality landing pages / ad destination pages which richly develop the intent of the ad, and the ads must include appropriate keywords that are reflecting in the landing pages, which brings us to dynamic ads.

Landing pages that are congruent with your ads have been important for a long time and has driven the trend of having your landing pages not only disconnected from your main website navigation, but also omitting your site navigation in order to focus the attention of the visitor only on the action you want them to take. This latest evolution is driving the use of 3rd party platforms which make it easier for digital marketers to setup dynamic landing pages, including sophisticated analytics around visitor behaviour.

Responsive Ads [and dynamic landing pages]
The latest overhaul of the Google Ad’s platform provides seriously sophisticated functionality for creating responsive ads – that is, ads whose content programmatically incorporates the search term entered by the searcher, from a set of search terms which you specify, and configures itself to the available space. A combination of 15 alternate headlines and 4 descriptions is possible. Using our back pain example, the responsive ad functionality allows you to create an ad which might have a headline of ‘Suffering from back pain’ or ‘Suffering from a sore back’ or ‘Suffering from a back injury’ all with the one ad i.e. you don’t need to create 3 ads to achieve this. Then, if the searcher types in ‘help with back pain’ your ad will appear as “Suffering from back pain”, or if they type in ‘recovering from a back injury’, your ad will appear as “Suffering from a back injury”. The descriptions in the content of the ads can similarly be filled on the fly to match the searcher’s intent as determined by AI.

To take this one step further, specialised landing pages can be configured to receive the search terms entered by the searcher, and then display those words in the appropriate place. Obviously you need to be careful when doing this to ensure that the dynamic content makes sense in all cases, particularly if you decide to add more keywords a few month’s later. But the end result is a much more relevant landing page, a higher quality score, and additional traffic to your site at a lower cost.

The ability to create responsive ads and content takes a bit of effort to setup, but means that you can create a lot more ads for less effort over the longer term, and achieve greater ad impressions, therefore more clicks, at a lower cost. It doesn’t however take away the need for greater copywriters, creative ideas and overall campaign strategy.

Ad Format
The available ad formats continue to evolve, and now includes:

  • Basic text-only ads.
  • Responsive ads – can insert text from a set of specified options matching the searcher’s search term, transform into text or image ads and automatically adjust size, appearance and format to fit space.
  • Image ads – static or interactive graphics, animated ads.
  • Image carousel ads.
  • Instream video ads – including vertical format ads specifically for people on mobile (you may need two versions), standalone video ads or inserted in streaming video content.
  • Product shopping ads – product photo, title, price, store name+ more details.
  • Showcase shopping ads – image and description that expands when clicked to show several related products and store information.
  • App Promotion Ads – drive app downloads and engagement with app promotion ads.

The ad formats available depends on your campaign type (search network, display network, search + display networks) and campaign sub-type (e.g. standard or all features).

Targeting
How we ensure ads are seen by the right people is continuing to evolve. In the Google Display Network (where websites show Google Ads, rather than ads as a result of customer searching) the placement of ads is much more a result of prior browsing activity and demographics (by users signed into Google) and less about their search terms.

Google’s “Exact Match” setting is no longer really exact … instead it works out intent using AI (read more about the dismantling of exact match over the years). This is a little annoying as taking that control away means that we are relying on the accuracy of the AI and ultimately Google wants you to spend more. It also means that the thorough use of negative keywords (preventing your ads from displaying when particular search terms are used) is even more important. We constantly review the search terms used to display ads, and extend the negative keywords list to prevent waste of our client’s ad budgets.

With Google’s significant improvement in targeting by audiences – whereby you load your known audience (customer database) up into Google and it then targets exactly those people with your ads, or builds matching audiences of similar people – due to it’s use of artificial intelligence, using this feature to target the right customers for your ads has become more useful. You can make the best use of this feature when you have a larger customer database, and when you know where each person or segment are in their buying journey, allowing you to present appropriate ads for each person. New demographics have been appearing in the audience settings including marital status, home ownership and the like, so we expect this area to continue to expand. Although the recent $5Bn fine against Facebook could slow things down in this area?

Many businesses use Google Ads primarily for top-of-funnel (prospects at the very start of a customer purchase journey) and then use other means to communicate with the new prospect such as email nurture programs. Not surprisingly, Google wants businesses to use Google ads all the way through the process. The use of Google re-marketing – presenting a similar ad to a person who has previously clicked on your ads and visited your website – is another cost-effective way to re-enforce your message with prospects, as these ads have significantly cheaper cost per click.

Bing have launched their own audience building feature this year, which is also AI powered across data collected from Bing searches, Skype, MSN and LinkedIn usage, and is not to be ignored for highly targeted campaigns.

Configuration
Setting up ad campaigns for mobile searches (more than half of all searches are carried out on a mobile phone) was previously cumbersome, requiring advertisers to create another set of ads just for mobiles. That’s changed with the new ad platform allowing the one ad, including ad extensions, to be customised within the one place for desktop and mobile.

Goal based campaigns allows the choice of the results you want to achieve – such as increased leads, greater brand awareness or higher conversions – and then Google will provide recommendations for campaign types that will perform best for you and your budget, and provides numerous automatic bid and placement optimisations. Again this is a result of their deepening use of artificial intelligence.

Whilst Google is giving us far more recommendations to use as we configure and optimise ads, sometimes these recommendations conflict; we don’t just follow these without careful consideration to ensure that they fit with your objectives.

Ad Extensions allow extra information to be shown as part of your ads. New ad extensions include Promotion Extension – the ability to include a price or special offer – thus enticing a visitor to click your ad instead of another.

Controls
Google is much more active in the assessment and banning of ads for all manner of legal and ethical reasons. We create ads with the best intentions in mind, include images, and then may need edit after Google has reviewed.

If you are using Click to Call style ads, the business name in your ads must now really be your business name, and mentioned in your IVR or by the person answering (sounds obvious, right … you would be surprised at how less-than-honest marketers have exploited this in the past). Interestingly, with the increase use of mobiles for search, Google removed the extra charge it originally levied on advertisers using click to call ads to provide metrics and reporting such as length of call (now a customisable setting to attribute as a conversion which previously not been tracked).

Summary
Due to the complexity of the ad platforms, increasing competition for organic search ranks, priority of the search companies to drive revenue through search, and the importance of the configuration on the cost of your pay-per-click ad campaigns, it is really important to keep a close eye on your pay-per-click ad campaigns. We work with our clients with pay-per-click campaigns in a variety of models, from strategic advice all the way through to full responsibility for creation and execution of ads. We can pick up your existing campaigns from where they currently are and improve them over time, or work with you to create your first ever pay-per-click ads.

We look forward to having a conversation.

But Wait! There’s More
We haven’t touched on You Tube ads in this article (also owned by Google). With the viewing of You Tube video continuing to grow, presenting video ads within You Tube is an option more organisations need to utilise.

Google does not stand still – it tends to roll out a major update to its ad platform every 6 months. Google has already announced the many new features coming to their ad platform which will likely be rolled out during the rest of this year. Many of these relate to search on mobile phones. Here’s a short list:

  1. A new type of ad – Discovery Ads – to appear in the new Google Discovery Feed app that mobile users are likely using on their phones. Discovery Ads, because they are interrupting people in a similar way to ads inside your Facebook Feed, will have strict quality constraints around them e.g. the requirement for unique (not stock) high quality images.
  2. Images inside search ads, but only on mobile, and only in the first place – this will be called a Gallery Ad.
  3. AI will be used to create interesting 6 second videos from original, up to 90 seconds in length.
  4. Deep links from ads to inside apps.
  5. Advanced bid strategies will allow you to exclude data considered by artificial intelligence when determining when to place ads e.g. particular spikes due to out-of-the-ordinary activities.
  6. Location based ads will start appearing in Google Maps search suggestions and while a user is use getting directions.

We look forward to exploring the use of these changes and more in the Google ads platform.

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The Fourth Industrial Revolution is Business as Usual

November 27th, 2018 by Heather Maloney

the fourth industrial revolution

I work in the industry that has been pronouncing or driving dramatic societal changes for the past 3 decades:

  1. The internet … I remember that catch phrase in the 90s of “if you aren’t on the internet within 12 months your business will be dead”.
  2. The year 2000 bug … stock your pantries, the world is about to end when equipment stops because of the general lack of support for a 4 digit year.
  3. Closely followed by the dot.com crash of the early 2000s caused by speculative, and outlandishly overvalued investments in technology companies.
  4. The internet of things was discussed around 2010 which predicted that within a short period of time all manner of things will be interconnected, utilising the internet, to provide a dramatically different way of living. That more “things” would be connected to the internet than people.

All of the above became kind of true (and the internet of things is still playing out / expanding), but not reaching the zenith proposed by the vendors of the theories; yes, vendors – many of the voices behind these ideas stand to gain by promoting the concepts.

So, it is no surprise that as we approach the end of this decade, my colleagues and others have started talking about the next big dramatic change – the 4th Industrial Revolution (‘4IR’). 4IR is fuelled by technologies such as artificial intelligence and machine learning, cloud technologies, and 5G. Many of those discussing 4IR predict the destruction of humanity as robots take over, leading to mass unemployment.

“The fourth industrial revolution is growing out of the third [the digital revolution] but is considered a new era rather than a continuation because of the explosiveness of its development and the disruptiveness of its technologies. According to Professor Klaus Schwab, Founder and Executive Chairman of the World Economic Forum and author of The Fourth Industrial Revolution, the new age is differentiated by the speed of technological breakthroughs, the pervasiveness of scope and the tremendous impact of new systems.”(1) Those talking about 4IR want us to embrace the new technologies and allow humanity to explore them to their fullest extent in order to achieve great improvements in the lives of everyone. Greater sharing of knowledge and resources than ever seen before is key, which in turn requires a new economic model to ensure that every person in the world has their needs met in a fair and equitable way. End environmental degradation, poverty, homelessness, hunger, and provide equitable access to education, nutritious food and significantly advanced medical care.

I listen to talk about 4IR with what I view is a healthy dose of skepticism. I agree that technology will continue to promote and support change at an exponential rate. But when it is all said and done, we are still humans, living our human lives. We naturally more easily understand, and therefore trust, people who are similar to ourselves – whether that’s the language we speak, where we went to school, where we have grown up, the details of our upbringing, our worldview. It is my expectation that moving to a new economic sharing model is the zenith that will never be anywhere near achieved, despite how attractive it may sound. We will not succumb to, or comply with, a small number of organisations controlling all the data and resources, and their distribution. Different groups of people will continue to evolve technologies to solve common problems, in different ways. We are already seeing push back – Google have recently been successfully fined a record 5 billion dollars in Europe for anti-trust. Also in Europe the GDPR legislation is pushing back against the will of corporations such as Google and Facebook to use data at their own discretion, just because they provided a free service when they were gathering that data.

It is my view that we will continue to explore the benefits of new technologies; some will make vast sums of money from new inventions and innovations, some will lose their jobs and need to retrain to do something else, but the vast majority of humans will keep living their lives with a few extra conveniences that modify what used to be the norm, and hopefully improved medical outcomes and a cleaner environment. On the whole we will become more knowledgeable; whether we become wiser remains to be seen.

I also believe that every type of industry will be affected by rapid technological developments. That includes technology companies! We are constantly under pressure to offshore, change technologies to the latest thing, provide services and solutions for very low cost or free. I am careful to not just jump onto the latest bandwagon for ourselves or our clients, but to make sober decisions which appropriately weigh up risks and benefits, and also reflect my personal values.

Change affecting business is nothing new. That’s part of running a business – recognising, adapting to, or taking advantage of the change going on around you is a fundamental skill of a business owner, those responsible for business strategies, and the corporate board.

(1) https://whatis.techtarget.com/definition/fourth-industrial-revolution

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How can Business (big and small) Harness Artificial Intelligence?

April 30th, 2018 by Heather Maloney

Futuristic city with delivery person sending off drones with packages from skyscraper
A recent Contactpoint blog post described the way in which artificial intelligence and machine learning are impacting our world at large and how it works. This blog attempts to answer the question “How can business, both small and large, utilise AI to make significant advancements?” AI is certainly not a technology only available for large corporations.

I assert that there are 3 main ways that your business can benefit from artificial intelligence (‘AI’):

  1. By integrating your website / app with software that has been improved by the use of AI. Such integration will significantly improve the value provided by your solution.
  2. By using software that has been improved by AI for running your business, thus significantly improving the manner in which you run your business, on an ongoing basis.
  3. By running your own deep learning exercises to determine the answer to a difficult question, which either improves your business performance or your understanding of your clients.

I expect that you already, perhaps unknowingly, use the outcome of AI or machine learning every day. Understanding it will help you harness it even more, so let’s explore just a few examples of each of these opportunities.

Integration
The Google search engine is underpinned by the use of AI – the more web pages it crawls, the better and better it gets at providing people with valid and useful search results. That’s part of the benefit of AI; traditional programming requires modification over time in response to the way people use it, with AI driven solutions, they learn and improve on their own.

Baidu, the so-called Chinese version of Google, allows you to upload an image, and request “similar images”. The search for similar images is not based on text around the images on a web page but solely uses the content of the images (2). Images, in technology terms, are made up of pixels of colour, which individually tell you very little. It is the manner in which the colours are combined, and the hard and soft edges around groups of pixels, which determine what is actually represented. AI underpins Baidu’s ability to find similar images – a very complex problem, and probably not something you could program a computer to perform. Traditionally the ability for a programmer to tell a computer how to achieve a goal was a prerequisite to solving that goal programmatically. With the use of AI, instead of telling the computer how to solve the problem, the program is allowed to train itself to solve the problem, getting better and better at achieving a task the more times it is performed.

We all use text search to find the things we need in Google or other search engines. It’s been possible to integrate the Google Search Engine into your website or app for many years, including restricting the search results to a particular domain or set of domains, thus providing excellent search results to your visitors without needing to write a search engine algorithm yourself. The ability to also search by images may be the differentiator that your website or app needs to deepen the value for your customers.

Other AI enriched applications that may enhance your application include:

  • Voice recognition – for speech to text and voice control of your app.
  • Language translation.
  • Image recognition e.g. Facebook suggesting name tags for people in photos you upload.
  • Route planning e.g. navigating from one place to another, taking traffic and other factors into consideration.

Clickup.com, a project management software, provides another example of integration. They announced this month that Clickup is now integrated with Alexa and Google Talk, allowing users to quickly interact with the online software by voice (3).
Google and Microsoft allow you to play with some of their AI enhanced functions via websites (4).

Operations
There are many functions that all businesses carry out. These functions are attracting the application of AI in order to make the tools used to complete these tasks, exponentially better than they have been before, and thereby attract new business.

Keeping up with the last news in your industry during your morning commute is now so much easier thanks to tools such as Voice Aloud which enables your smart phone to read an article to you while you drive (carefully of course). Your smart phone will also allow you to search using voice commands, using Google Voice Assistant or the iPhone Siri, allowing you to search hands free.
I recently asked my Android phone “Okay Google, what do I have on today?” expecting to have a list of my appointments read to me – it did that and, then started playing me 2 – 3 minute snippets of daily news recorded by various news agencies around Australia. It was a fantastic way to keep up-to-date and it “learned” that behaviour all on its own.

Google Search enables you to find relevant information, and this search is very accurate, powered by AI. It’s very important for Google’s revenue from online advertisements that Google Adwords provides relevant ads to searchers, because it is the relevance that inspires people to click on an ad, thus earning Google revenue. Similarly ads which appear in amongst Facebook news feeds are very reliable for showing your ad to the right audience, and once you have achieved excellent click through the result of Facebook’s AI research ensures that it will promote your ad to “look-a-like” audiences, based on what it knows about the people who already clicked. You can now much more confidently spend money on pay-per-click, because you can tailor your ads to specifically targeted audiences.

In a recent Contactpoint blog we talked about chatbots – the best of these are underpinned by AI, improving their results the more they are used so that they can help answer an inbound question before the human gets involved.

A number of online customer service and customer relationship management tools are now underpinned by AI. In these functions AI is bringing valuable insights as you use the tools, such as:
– Which clients are at the greatest risk of leaving you? (5)
– Which phrases and styles of interacting with customers produce the greatest sales results?
– What are the most important additional products or services to provide to your customers?

The better banking and financial management tools are now underpinned by AI to help you identify fraud (6). Similarly computer networks are being better secured from intrusion, viruses and malware now by solutions that use AI to detect unusual behaviour (7).

If your operations involve designing products and engineering, AI is making great inroads into design tools to help speed up the process (12).

Actionable Insights / Solving Problems
So far we have considered AI lead improvements to more general problems. Your business will be operating in a particular domain in which you are an expert, and in which there are very specific problems that have not yet been solved, or can’t be solved quickly & reliably for a large number of customers. This is where the power of AI may be the most potent, because machine learning / deep learning can be used to arrive at breakthroughs in your particular domain. Whilst it helps if you have lots of data in order to feed the deep learning process, for smaller businesses, you may be able to access public data to achieve the same goal, or use pre-existing neural networks to solve your similar problem.

Tools such as Chorus.ai are ready to take your organisation’s live data, in order to provide you with valuable insights in a specific operational area (8). In the case of Chorus.ai it analyses your meetings, particularly sales meetings, to help you get the best performance out of future meetings.

AI is being used to great effect by large corporations such as Walmart to quickly respond when high turnover products look like running out of stock, recently reporting a year-on-year 63% increase in sales (10).

Smaller organisations are also using AI to gain actionable insights, including a Zoo which now has a much better accuracy in predicting high attendance, and therefore staffing requirements, based on using AI to determine all the factors (not just weather) that increase visitor rates (10).

Domo is a tool created to help businesses, small and large, collate data from a wide range of sources (social media, ecommerce, chat bots etc), and help an organisation spot trends in real time (11).
In the area of product design and engineering, a concept called Generative Design underpinned by AI, is enabling faster design and many more possible designs to choose from by allowing all the constraints of the product to be entered, and then allowing the program to generate a large number of possible solutions (13).

However, for a problem more specific to your industry or expertise, you may need to perform a highly customised deep learning experiment. Once you have determined the question you need to answer for your specific area of expertise and industry, there are 7 steps in performing your own AI or deep learning experiment:

  • Gathering data
  • Preparing the data
  • Choosing an AI model to suit your question / domain
  • Training the model with data which contains the results / answers
  • Evaluation of the performance of the model
  • Tuning of the factors determined by the model
  • Applying the model to fresh data in order to gain insights or greater performance. (1)

As a business owner or leader you should be considering the way in which artificial intelligence or machine learning can change the way in which you operate and solve your customer’s problems. Don’t hesitate to get in contact if you would like to discuss how AI can be put to work for your organisation.

Read this article to understand more about how machine learning works, and how artificial intelligence is impacting our world.

If you would like to discuss how AI might benefit your organisation, please don’t hesitate to get in touch with Heather Maloney.

References:
(1) https://towardsdatascience.com/the-7-steps-of-machine-learning-2877d7e5548e
(2) https://www.wired.com/2013/06/baidu-virtual-search/
(3) https://clickup.com/blog/alexa-google-assistant-project-management/
(4) https://aidemos.microsoft.com/ & https://experiments.withgoogle.com/ai
(5) http://www.bizdata.com.au/customer-smartdetect?gclid=CjwKCAjwlIvXBRBjEiwATWAQIseaubDdIaMGue_bjuC9BZU3WLErB1Qj9u12XKmkZGZPz-UGO4balhoCaxgQAvD_BwE
(6) https://www.techemergence.com/machine-learning-fraud-detection-modern-applications-risks/
(7) https://www.techemergence.com/network-intrusion-detection-using-machine-learning/
(8) https://www.chorus.ai/product/
(9) https://www.morganmckinley.com.au/article/how-ai-helping-small-business-today
(10) https://www.clickz.com/5-businesses-using-ai-to-predict-the-future-and-profit/112336/
(11) https://www.techemergence.com/ai-in-business-intelligence-applications/
(12) https://www.engineersrule.com/solidworks-puts-artificial-intelligence-work/
(13) https://www.autodesk.com/solutions/generative-design

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How the pursuit of Artificial Intelligence is changing our world.

April 25th, 2018 by Heather Maloney

The goal of achieving artificial intelligence – a computer that can learn and respond like a human – began in the 1950s(1). However it is only in the last few years that we have seen great leaps forward towards this goal. The reason for the sudden improvements is attributed to break through in an area of technology called neural networks – programming that attempts to mimic the way the brain works, and a feature of the area of machine learning.

Up until the use of neural networks and machine learning, the act of programming a computer to perform a particular task – think displaying words on a screen, adding up columns of numbers, changing an image from colour to black and white – has required that a programmer can describe in exact detail the process of achieving that task. The human brain performs many tasks, seemingly effortlessly, that are virtually impossible for anyone to describe how they are done, beyond some vague concepts and pointers in the right direction. That’s not sufficient to be able to program a machine to do the task. Consider the task of identifying one human face from another – can you describe how your loved one looks, sufficient that another person who has never met them could pick them out in a crowd with any certainty? Very difficult! This is just one example of how amazing the human brain is when it comes to rapidly processing large amounts of information. We perform many such complex tasks almost simultaneously, without even realising.

A neural network is a programmatic attempt to replicate the manner in which it is believed the brain performs complex tasks. The diagram below is a typical representation of a neural network used to carry out a particular task. As an example, consider an input being an image of a face of a person who just passed the camera, and the task to be performed by the neural network being determining whether the image is “Joe Citizen”. The first round of analysis processes the input (camera image of a face) and then passes information about that image in the form of weightings down to the next level of processing. The second level receives that analysis, performs further analysis, and then passes another set of weightings down to the next level, and so on until the end result, which is the most likely answer to the question posed at the outset (where the attributes of Joe Citizen is already known by the program)? The “hidden layers” may comprise many different layers to allow deeper and deeper analysis and greater refinement aimed towards arriving at the correct answer.

Neural network diagram

Machine learning involves allowing a computer program to learn by working through a large amount of data, which also contains the answer to a particular question e.g. data on the observations of humans who both did and did not contract a particular disease in the future. The machine learning program will build a neural network of weightings required to answer the question being posed. Then that neural network is put to work against fresh data to further refine the learning, including humans providing feedback on the program’s accuracy. Finally, armed with all that learning stored in a neural network, the program can then be applied to new, live data in order to interpret that data … it turns out, with great speed and accuracy, surpassing that of humans (1).

The above is a very simplistic description of the way neural networks operate; computer scientists involved in the use of neural networks are constantly improving their performance. Neural networks are still in relatively early days of development, and already there are many different neural network models to choose from, some better at particular problem types compared to others.

An important distinguisher in neural networks compared to “regular” programming is that the neural network can be relatively easily tuned to perform better over time, as well as “learning” from more and more data. A “regular” computer program needs to be manually reprogrammed as requirements change, again requiring someone to describe exactly what is required, and understand all the implications of that change throughout the system.

Machine learning has been applied in the last few years, with great affect, in the following areas:

  • Image / Facial recognition – ever thought about how the image search feature of Google Images, or the speedy face tag suggestions by Facebook upon upload of a photo, have become so good? A person wanted for an alleged crime in China was picked up by security cameras in about 10 minutes of the wanted person entering a concert earlier this month (3).

    City Deep Learning
  • Navigation & self-driving cars – being able to respond to incoming information, such as what other road users are doing around you, is essential for solving the problem of self-driving cars. The amount of technology involved in an autonomous car is awesome – and it needs to be given the life and death involved. “Even if it will take some time for fully autonomous vehicles to hit the market, AI is already transforming the inside of a car.” It is predicted that AI will first bring to our cars a host of so called “guardian-angel” type features to reduce the likelihood of accidents (11).
  • Speech recognition – in the last few years speech recognition (at least for native English speakers) has become very accurate, requiring very little training for a particular person. I now control my mobile phone using voice on a regular basis, because talking to my phone is much faster than typing – apparently 3 times faster according to a study by Standford University (4). Google’s latest speech-to-text system, called Tacotron 2, will add inflection to words based on punctuation to further improve understanding (5) and making it even more human-like when it is reading text to you, or responding with an answer to a question. Speech recognition in devices such as Google Home and Amazon Alexa are making simple tasks much easier. The article entitled “Amazon Echo has transformed the way I live in my apartment – here are my 19 favourite features” shows how speech recognition is being used for hands-free computer assistance in a simple home context (9). Applications of this technology are vast and life-changing for those who don’t have free hands (e.g. a surgeon at work) or are not able to type.
  • Prediction – more quickly and accurately diagnosing a current situation or predicting that a current set of information is an indicator of a future state e.g. in diagnosing disease, predicting financial market movements, identifying criminal behaviour such as insurance or banking fraud (13). The ability of a neural network to process vast amounts of data quickly, and build its own conclusions with regard to the impact of one factor on another (learn) is already helping doctors to more accurately diagnose conditions such as heart disease (12). Reducing the acceptable level of inaccuracy in medical diagnosis will lead to much better patient outcomes and reduce the cost of healthcare to our ageing population.
  • Playing games – a lot of AI research uses games to work out how to train a computer to learn. (8) From time to time I play an online version of the Settlers of Catan board game; when players leave the game (ostensibly because they have lost internet connection … usually it’s when they are losing!), you get the option to continue to game and have AI finish it on their behalf. It amuses me that I find myself, and others, immediately ganging up on the AI player. I mean, they won’t care if you make their game difficult – they’re a robot after all! It was actually the success of a computer to beat the best human players of the hardest game we play that heralded the success of artificial intelligence, and made the world take notice of its capabilities (14). “In the course of winning, [the robot] somehow taught the world completely new knowledge about perhaps the most studied and contemplated game in history.”

But, will the rise of artificial intelligence take away our jobs? Some say yes, others say no (6), but they all say that the new jobs created due to artificial intelligence will be different to current roles, and require different skills (7).

Worse than job loss, will AI cause a computer vs human war or lead to our extinction? Elon Musk is well known for his warnings against AI. It could be viewed that the pressure he has applied to the technology industry helped to lead to an agreement that the technology giants will only use AI for good (10).

I don’t believe that AI will ever result in a computer takeover of the world, because there is more that makes humans different from other animals … not just our ability to think. Reproducing just our ability to think, learn and make decisions, even in a super-human way, does not make a computer human. The capacity for machine learning / deep learning to significantly improve our lives, particularly in the areas of health and solving some of our most challenging problems, is exciting. However, I believe that it is right to be cautious; to move ahead with the knowledge that machine learning could also be used for harmful purposes. Computers can also “learn” the negative elements of humanity (15).

Business owners, innovators and leaders should consider how machine learning might be harnessed for your organisation in order to provide better value, predict more accurately, respond more quickly, or make break-throughs in knowledge in your problem domain. Let’s harness artificial intelligence for good! Read more about “How Business (big and small) can Harness Artificial Intelligence“.

References:
(1) https://www.forbes.com/sites/bernardmarr/2016/12/08/what-is-the-difference-between-deep-learning-machine-learning-and-ai/#4cc961d726cf
(2) https://hbr.org/cover-story/2017/07/the-business-of-artificial-intelligence
(3) http://www.abc.net.au/news/2018-04-17/chinese-man-caught-by-facial-recognition-arrested-at-concert/9668608
(4) http://hci.stanford.edu/research/speech/index.html
(5) https://qz.com/1165775/googles-voice-generating-ai-is-now-indistinguishable-from-humans/
(6) http://www.abc.net.au/news/2017-08-09/artificial-intelligence-automation-jobs-of-the-future/8786962
(7) http://www.digitalistmag.com/iot/2017/11/29/artificial-intelligence-future-of-jobs-05585290
(8) https://www.businessinsider.com.au/qbert-artificial-intelligence-machine-learning-2018-2
(9) https://www.businessinsider.com.au/amazon-echo-features-tips-tricks-2018-2
(10) https://www.vanityfair.com/news/2017/03/elon-musk-billion-dollar-crusade-to-stop-ai-space-x
(11) http://knowledge.wharton.upenn.edu/article/ai-tipping-scales-development-self-driving-cars/
(12) https://www.telegraph.co.uk/news/2018/01/03/artificial-intelligence-diagnose-heart-disease/
(13) http://bigdata-madesimple.com/artificial-intelligence-influencing-financial-markets/
(14) https://deepmind.com/research/alphago/
(15) https://www.theverge.com/2016/3/24/11297050/tay-microsoft-chatbot-racist

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