It's quite possible you're sick of hearing about artificial intelligence (AI) and how it could transform your business. But away from the marketing hype, there are sound reasons to start investigating how AI could benefit your company.
The first step to understanding what the fuss is about and separating the signal from the noise is jettisoning the term AI. While 'AI' describes an academic field devoted to studying how to build intelligent machines, it's a loosely defined term, leaving room for unscrupulous vendors to rebrand legacy software by throwing AI into the sales pitch.
"With people using AI to describe pretty much everything, this is where the hype comes in," says Dr Panos Constantinides, associate professor at Warwick Business School. "The hype revolves around the lack of clarity as to what we mean by AI," he added.
To sidestep that confusion, it's better to be more specific: what most tech vendors mean today when talking about AI is machine learning (ML).
Machine learning is a subset of AI and can be used to teach computers how to carry out a wide range of tasks by analysing large volumes of data, rather than following instructions laid out by a programmer in a piece of software.
Interest in machine learning has exploded thanks to recent breakthroughs in areas like computer vision, speech recognition, and natural language understanding. Fuelling these advances are new ways of carrying out machine learning, such as deep learning, which in turn has been made possible by the power of modern processors and the large quantities of data that organisations can now collect.
In theory, machine learning holds the promise of automating large areas of work that until recently were manual processes. Handling customer contact centre queries, back-office administration roles, even eventually driving vehicles -- at least on simple stretches of road like highways.
The reality is, however, that many businesses are a long way from implementing a machine-learning powered system in production. A survey for the O'Reilly AI Adoption in the Enterprisereport found that just under 75 percent of respondents said their business was either evaluating 'AI' or not yet using 'AI', although the stage of use did vary by industry:
Then there are the more prosaic uses of machine learning that have been in place for years: in the recommendations systems used by Amazon to get you to buy more products, and by Netflix to get you to watch more shows; and in the global security systems run by the likes of Microsoft to flag online threats as they emerge. More recently, financial investment firms like Citigroup have also started using machine learning to spot fraudulent transactions and errors in payments.
It's quite possible your firm already uses a service that at least partially relies on machine learning, particularly as vendors augment existing services to include new features powered by ML's prodigious pattern-matching capabilities. Examples might be the use of ML in natural language processing and speech recognition for chatbots and other automated response systems in customer contact centres, or to spot spam and autocomplete sentences in email services. Indeed, respondents to the O'Reilly report named customer service and IT as two of the most common areas where their firm was using 'AI'.
Other companies are experimenting with using machine learning to model repetitive tasks carried out by employees, in an attempt to automate those tasks using software. There are already companies that specialise in this area, which is known as Robotic Process Automation (RPA). In the report Automation, AI, And Robotics Aren't Quick Wins J P Gownder, VP at analysts Forrester, gives the example of a German pharmaceutical company that uses RPA to automate the procurement process.
RPA doesn't always involve machine learning, and historically been carried out by developers spelling out the rules for automating the process in software, rather than those rules being learned by the system. So while automation shouldn't be confused with machine learning (as the steps to automate a process could have been coded by a developer), Forrester predicts a greater role for ML in RPA in future.
"Firms are already combining AI building block technologies such as ML and text analytics with RPA features to drive greater value for digital workers," states Forrester's Predictions 2019: Artificial Intelligencereport. The analyst firm predicts a role for chatbots in controlling RPA software, machine learning models that spot patterns in Internet of Things (IoT) data to trigger 'digital workers', and the use of text analytics to increase RPA's capabilities.
But, at present, companies using machine learning in production systems appear to be the outliers, with the majority of firms only trialling ML systems or simply using services like Gmail that include some ML-powered features.
"Companies are doing Robotic Process Automation -- there's a reasonable uptake, 20-30 percent of company processes are in my estimation being automated that way, but the uptake of machine learning is in very small areas," says Mark Skilton, professor of practice at Warwick Business School.
That said, companies seem to be aware that there's potential for machine-learning systems to open up new efficiencies, services and products in the coming years: the O'Reilly report found that just under two thirds of respondents say their company plans to invest at least 5 percent of their IT budgets in 'AI projects' over the next year.
Companies told Forrester that their main priority for investing in automation were cost savings, as you can see in this extract from the Automation, AI, And Robotics Aren't Quick Wins report. Below are the answers when companies were asked: 'What are or could be the biggest benefits of adopting automation technologies for your organization?'
How to get started?
Of course, it would be foolish to adopt machine learning without being clear on why you're doing it. So what exactly can you do with machine learning?
Machine learning models are typically tasked with spotting patterns in large volumes in data. In practice, this pattern-recognition ability has resulted in systems that can pick out words from audio, people from photographs, and understand a word's meaning in a sentence -- to give just a few examples.
You'll need a mix of domain expertise and in-house data science skills to get started, with the first steps being to decide what you want to achieve, whether machine learning is a good fit and, if you're not using an on-demand service, which category of ML to use -- supervised, unsupervised or reinforcement learning.
There are a number of considerations before starting on a project: What data are you collecting?'; 'How can that data be transformed to make it suitable for training a machine learning model?'; and 'What features of that data are going to be of interest for training your machine-learning model?'.
"You can't expect that data are ready made," said Constantinides. "It's data scientists who will create the categories that the machine-learning algorithm will be looking for. If you can't get data right, you don't have a successful application of machine learning."
There's also the question of whether using existing data to train a model would require you to seek further permissions, or impose additional protections to comply with privacy regulations such as the EU's General Data Protection Regulation (GDPR).
"The way data are aggregated makes it very difficult to know exactly where the data came from or how decisions are made in particular cases," said Constantinides, adding that gaining proper consent with GDPR could be particularly challenging when training deep neural networks. Just one area where GDPR throws up additional barriers to the use of machine-learning based technologies is the use of facial recognition by retailers in stores.
When it comes to the technical choices, you'll need to decide whether to rent hardware in the cloud or build your own deep-learning rig. The major cloud providers -- Amazon, Microsoft and Google -- offer a range of on-demand, pay-per-use machine-learning services.
These services cover speech recognition, computer vision (such as object, face, and emotion recognition), natural language processing (the ability to interpret human language), sentiment analysis, data forecasting and translation. Sometimes these services are bundled up into higher-level more sophisticated offerings, such as chatbot creation kits and recommendation engines for retailers.
Beyond the on-demand services, each of the major cloud platforms, including AWS, Google Cloud, and Microsoft Azure, also offer services that allow firms to train and run machine learning models using their cloud infrastructure. These models can be applied to any data the firm requires, although doing so will require in-house data scientists to work with domain experts and IT ops staff to decide where machine learning could be used most effectively, and to design a process for preparing data, training and deploying the machine-learning model.
The cloud platform providers have even started offering services that partially automate the training of machine-learning models, although these are aimed more at augmenting the skills of data scientists than replacing them. These offerings streamline the process of training a machine-learning model via drag-and-drop tools and other simplifications, with services including Microsoft's Machine Learning Studio, Google's Cloud AutoML and AWS SageMaker. Meanwhile, preparing data for training a machine-learning model -- labelling images in a computer vision task, for example -- is often contracted out to freelancers via crowdworking websites such as Amazon Mechanical Turk.
If you do decide to build your own machine-learning system in-house, it won't be cheap but may be more affordable than using a cloud service if you anticipate the training process will require more than a couple of months of intensive work.
You'll need to invest in a decent GPU to train anything more than very simple neural networks -- the brain-inspired mathematical models that underpin machine learning. GPUs are typically necessary to train neural networks thanks to their ability to carry out a very large number of matrix multiplications in parallel, which helps to accelerate a crucial step during training.
If you're not planning on training a neural network with a large number of layers, you can opt for consumer-grade graphics cards, such as the Nvidia GeForce GTX 2060, which typically sells for about £320, while still offering 1,920 CUDA cores.
More heavy-duty training, however, will require specialist equipment. One of the most powerful GPUs for machine learning is the Nvidia Tesla V100, which packs 640 AI-tailored Tensor cores and 5,120 general high-performance computing CUDA cores. These cards cost considerably more than consumer alternatives, with prices for the PCI Express version starting at £7,500.
Building AI-specific workstations and servers costs an order of magnitude more, for example, Nvidia's deep-learning focused DGX-2 packs 16 Tesla V100 cards and sells for $399,000.
There are a wide range of deep-learning software frameworks, which allow users to design, train and validate deep neural networks, using a range of different programming languages.
A popular choice is Google's TensorFlow software library, which allows users to write in the Python, Java, C++, and Swift programming languages, can be used for a wide range of deep-learning tasks such as image and speech recognition, and which executes on a wide range of CPUs, GPUs, and other processors. It's well documented, and has many tutorials and implemented models that are available.
Another commonly used framework, especially for beginners, is PyTorch, which offers the imperative programming model familiar to developers and allows programmers to use standard Python statements. PyTorch works with various types of deep neural networks, ranging from CNNs to RNNs, and runs efficiently on GPUs.
What types of project could firms use to experiment with machine learning? Constantinides recommends starting simple, focusing on a non-critical area of the business, and then scaling up from there.
While the nature of the project will depend heavily on the industry sector, Constantinides cites a contact centre chatbot as an example of a simple project for many businesses.
This chatbot could handle straightforward, repeated customer queries and hand the customer off to a human operator if the query gets too complex. It would use natural-language processing to enable it to deal with more complex interactions than the old rules-based chatbots.
"Most companies consider call centre support as a secondary function that's outside the core competencies of the organisation," Constantinides said. "As such, it's considered a low-risk use case."
From that starting point, companies could move to another ML-powered service -- recommendation engines -- using data from customer interactions to push customers towards other products and services, Constantinides added.
"From there they can scale up. Once you have all this data about your customers through these customer interactions, then you can start making different kinds of predictions. You can start asking different kinds of questions. Leading questions, like 'Would you consider buying this other product?' or 'If you're already satisfied with this service, then why don't you consider this?'. So, from customer support it changes to dynamic marketing. You're building on top of this initial use case."
In a similar vein, Forrester's Gownder also highlights the importance of narrowing the focus of any starter projects to a specific task. In the report Automation, AI, And Robotics Aren't Quick Wins he gives the example of a healthcare tech firm focusing on analysing medical scans for radiologists, rather than setting the broader and less manageable goal of tackling cancer as a whole.
In general, it's important to keep your expectations in check when using machine learning-powered technologies and to realise they will rarely deliver perfect results: speech recognition makes transcription errors and facial-recognition systems often misidentify people outside of strictly controlled conditions. These shortcomings are why many of these systems are often talked about as augmenting human judgement, as narrowing down the choices a person has to make, rather than replacing the person outright. There may be fewer humans in the loop, but full automation of many roles isn't feasible, at least for now.
The perils of automating too much, too fast are also flagged by Gownder in the Forrester report, which cites carmaker Tesla's move to restore humans to its production line after robots were found to be unsuitable for certain tasks.
"Since restoring humans to the production line, however, Tesla's Model 3 became one of the best-selling cars in America, growing from only 1,825 cars produced in January 2018 to 14,250 in July 12," Gownder writes.
A further complication facing firms may be finding the data science expertise necessary to implement machine learning projects. Over half of the respondents to the O'Reilly survey said their organizations were in need of machine learning experts and data scientists, for example. In a separate O'Reilly report, Evolving Data Infrastructure, data science and data engineering were again named as the two areas where companies suffered the biggest analytics-related skills gap.
"The technology and the promise is there -- the problem is really the tagging of the data and having the knowledge and the skills within the company to understand 'How do I prepare my data so I can start learning from it?'," said Warwick Business School's Skilton.
Despite these wrinkles, companies are increasingly experimenting with machine-learning technologies. According to Skilton, 2019 is a good year for companies to get stuck into the challenge of machine learning "so they can move the dial from human knowledge to machine knowledge, to augmenting people and making them more productive".