“BEFORE you discard of your horse and buy an auto it is well to think of the cost. Figure how much you spend for harness and the. Think of what new tires amount to.” —circa 1915
Similar erroneous logic is used today on why companies should conservatively invest on AI.
85 per cent of UK organisations plan to invest in artificial intelligence and the internet of things in the next three years, Half of organisations to invest more than £10 million in digital by 2020
Just one in ten executives believe that UK is a world leader in digital
Only 20 per cent believe there are enough school leavers and graduates entering the labour market with the appropriate digital skills and experience.
Eighty-five per cent of senior executives plan to invest in artificial intelligence (AI) and the internet of things (IoT) by 2020, according to a new survey of UK digital leaders by Deloitte.
Many people are unsure about exactly what machine learning is. But the reality is that it is already part of everyday life. A form of artificial intelligence, it allows computers to learn from examples rather than having to follow step-by-step instructions.
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Many people are unsure about exactly what machine learning is. But the reality is that it is already part of everyday life. A form of artificial intelligence, it allows computers to learn from examples rather than having to follow step-by-step instructions. The Royal Society believes it will have an increasing impact on people’s lives and is calling for more research, to ensure the UK makes the most of opportunities. Machine learning is already powering systems from the seemingly mundane to the life-changing. Here are just a few examples.
1. On your phone
Using spoken commands to ask your phone to carry out a search, or make a call, relies on technology supported by machine learning. Virtual personal assistants – the likes of Siri, Alexa, Cortana and Google Assistant – are able to follow instructions because of voice recognition. They process natural human speech, match it to the desired command and respond in an increasingly natural way.The assistants learn over a number of conversations and in many different ways. They might ask for specific information – for example how to pronounce your name, or whose voice is whose in a household.Data from large numbers of conversations by all users is also sampled, to help them recognise words with different pronunciations or how to create natural discussion.
2. In your shopping basket
Many of us are familiar with shopping recommendations – think of the supermarket that reminds you to add cheese to your online shop, or the way Amazon suggests books it thinks you might like.
Machine learning is the technology that helps deliver these suggestions, via so-called recommender systems. By analysing data about what customers have bought before, and any preferences they have expressed, recommender systems can pick up on patterns in purchasing history. They use this to make predictions about the products you might like.
3. On your TV
Similar systems are used to recommend films or TV shows on streaming services like Netflix. Recommender systems use machine learning to analyse viewing habits and pick out patterns in who watches – and enjoys – which shows. By understanding which users like which films – and what shows you have watched or awarded high ratings – recommender systems can identify your tastes. They are also used to suggest music on streaming services, like Spotify, and articles to read on Facebook.
4. In your email
Machine learning can also be used to distinguish between different categories of objects or items. This makes it useful when sorting out the emails you want to see from those you don’t. Spam detection systems use a sample of emails to work out what is junk – learning to detect the presence of specific words, the names of certain senders, or other characteristics. Once deployed, the system uses this learning to direct emails to the right folder. It continues to learn as users flag emails, or move them between folders.
5. On your social media
Ever wondered how Facebook knows who is in your photos and can automatically label your pictures? The image recognition systems that Facebook – and other social media – uses to automatically tag photos is based on machine learning. When users upload images and tag their friends and family, these image recognition systems can spot pictures that are repeated and assigns these to categories – or people.
6. At your bank
By analysing large amounts of data and looking for patterns, activity which might not otherwise be visible to human analysts can be identified. One common application of this ability is in the fight against debit and credit card fraud. Machine learning systems can be trained to recognise typical spending patterns and which characteristics of a transaction – location, amount, or timing – make it more or less likely to be fraudulent. When a transaction seems out of the ordinary, an alarm can be raised – and a message sent to the user.
7. In hospitals
Doctors are just starting to consider machine learning to make better diagnoses, for example to spot cancer and eye disease.
Learning from images that have been labelled by doctors, computers can analyse new pictures of a patient’s retina, a skin spot, or an image of cells taken under a microscope. In doing so, they look for visual clues that indicate the presence of medical conditions.
This type of image recognition system is increasingly important in healthcare diagnostics.
8. In science
Machine learning is also powering scientists’ ability to make new discoveries. In particle physics it has allowed them to find patterns in immense data sets generated from the Large Hadron Collider at Cern. It was instrumental in the discovery of the Higgs Boson, for example, and is now being used to search for “new physics” that no-one has yet imagined. Similar ideas are being used to search for new medicines, for example by looking for new small molecules and antibodies to fight diseases.
The focus will be on making systems that perform specific tasks well which could therefore be thought of as helpers.
In schools they could track student performance and develop personal learning plans.
They could help us reduce energy usage by making better use of resources and improve care for the elderly by finding more time for meaningful human contact. In the area of transport, machine learning will power autonomous vehicles. Many industries could turn to algorithms to increase productivity. Financial services could become increasingly automated and law firms may use machine learning to carry out basic research. Routine tasks will be done faster, challenging business models that rely on charging hourly rates.
Over the next 10 years machine learning technologies will increasingly be part of our lives, transforming the way we work and live.
Originally published by By Dr Sabine Hauert. Royal Society at BBC Technology
Long before the advent of today’s smart wrist wearables, we saw Hollywood’s James Bond using his watch to receive messages from headquarters, and long before any company began prototyping connected cars, we saw him in high-speed car chases by navigating through the streets in his smart car augmented with sensors.
Previously the domain of fantasy, such devices are now becoming a reality as a new class of tools accessible to the government, consumers and businesses alike.
The Internet of Things (IoT) has rapidly become one of the most familiar expressions across the technology domain, with the potential to fundamentally shift the way we interact with our surroundings. Everyday objects are getting smarter and connected to the internet, thereby enabling the seamless transfer of information streams between devices, networks, organisations, industries and end users.
Key Trends in IoT 2018
IoT in Healthcare
What is the Future of IoT in 2020
It is predicted that by 2020, more than 50 billion devices (‘things’) will be connected, with revenues from IoT forecasted crossing 3 trillion USD the same year. This rapid adoption of IoT brings with it a new set of challenges, which raise questions about where and how these devices should be used. But first, it is crucial to understand the functionality of IoT and its implications on aspects of everyday life.
Businesses across the world are moving rapidly to connect their products and equipment to the Internet-of-Things, opening up opportunities to create new business models and transform how they run their operations and engage with customers. However, tapping into the IoT is only part of the story. For companies to realise the full benefits of IoT enablement, they need to combine IoT with rapidly-advancing Artificial Intelligence technologies, which enable ‘smart machines’ to simulate intelligent behaviour and take well-informed decisions with little or no human intervention.
Over the coming years, ongoing advances in AI will have profound impacts on jobs, skills and HR strategies in virtually every industry—underlining the fact that companies don’t have the luxury of time as they map out their plans for an AI-enabled world. Already, integrating AI into IoT networks is becoming a prerequisite for success in today’s IoT-based digital ecosystems.
So businesses must move rapidly to identify how they’ll drive value from combining AI and IoT—or face playing catch-up in years to come.
In a recent thought leadership paper¹, we described why the advent of the Industrial Internet of Things (IIoT) is a once-in-a-lifetime business disruption one that requires organisations to develop or acquire new capabilities in managing direct relationships with customers, supported by transformed operating and business models. But realising the promise of the IoT also requires something else. To achieve its full potential, the IoT needs to be combined with an equally powerful and disruptive set of technologies categorised as Artificial Intelligence (AI). The exponential growth of the IoT is well-known, as underlined by the projections in the accompanying information panel. However, less widely appreciated is the profound impact that AI will have on every aspect of our personal and working lives—an impact that will be magnified and multiplied by its combination with the IoT. In fact, the titanic shift and ongoing disruption caused by AI is set to be similar to that caused by the introduction of the personal computer in the 1980s. Like the PC, AI will lay the foundations for an immense acceleration in innovation throughout and beyond the coming decade, creating a significant boost for the global economy. In the 1980s, nobody could have fully imagined the broad and deep changes that PCs would bring to our lives. Similarly, few people today can envision what AI will mean to us over the coming decades.
The value proposition of IoT and AI
Smart sensors: Delivering pervasive benefits
The mutually beneficial relationship between IoT and AI is manifesting itself in many successful integrations of the two technologies in the B2B and B2B2C space. The value propositions that underpin the fusion of IoT and AI include smart sensors (or intelligent sensors), which combine IoT and AI to provide realtime data and feedback that enables systems to fulfil the three capabilities we highlighted above—namely:
Predictive: Real-time data can be analysed to determine when a large piece of machinery or equipment will break down, enabling the failure to be prevented through proactive intervention. For example, today a GE jet engine collects 500Gb of data per flight, taking a ‘snapshot’ every second of over 5,000 parameters including air speed calibration, altitude, cooling, exhaust gas temperature and flow, and ground speed. This is in stark contrast to previous generations of jet engine technology, where just 1 kb of data was generated per flight from three snapshots (take-off, cruising, landing) on 30 parameters. The resulting insights enable GE to boost performance by 287 times while also delivering a seven-fold reduction in costs.
Prescriptive: Intelligent sensors can suggest immediate action at the edges of the organisation, thus avoiding outages and even disasters. For example, sensors on railway tracks can warn the control centre of any track failures. Similarly, lane centring technology in cars self-corrects when the driver veers away from the centre of the lane.
Adaptive/autonomous: Continuous data feeds from sensors can enable systems to learn the right actions to take autonomously. For example, in a healthcare context, blood glucose sensors can automatically change the level of insulin delivered in response to patient need. Similarly, monorail systems in many airports and cities run autonomously without any human drivers.
IoT / AI applications will impact all industries
Given the scale and range of potential benefits on offer, it’s hardly surprising that companies in many industries are beginning to take steps to seize the opportunities presented by combining IoT and AI
Time to create a strategic plan for IoT/AI
The combined disruption from AI and IoT will reshape our personal and business lives in a dramatic manner that is not fully imaginable or comprehensible by most companies today.
At one end of the scale, it will displace routine, monotonous human jobs with machines. At the other, it will radically disrupt the competitive landscape, by giving the early adopters of AI tremendous advantages in terms of lower costs, better customer experiences and a head start in pursuing new business opportunities.
While the full impacts of this disruption will not arise overnight, they will come a lot faster and sooner than most businesses and individuals are currently expecting. So, smart companies and executives are not waiting for the tsunami of disruption to reach their shores before they react. Instead, they are moving now to start the strategic dialogue needed to fully understand and prepare
for the disruptions before they arrive. Companies that take this proactive, far-sighted approach can turn the upcoming disruptions from an irresistible force that could sweep them away, into a massive
opportunity that they’re well-placed to realise. Put simply, the AI revolution is here—and now is the time to get ready for it.
From Siri to Alexa, customers are becoming accustomed to AI-powered solutions and soon they will expect the same for their local businesses. Sure, an AI roll-out can be daunting, but by adopting a strategic approach and adding smart software, small businesses will not only be able to differentiate themselves from competitors, but compete with the industry giants as well.
“While many over complicate the technology, AI’s behaviors are predictable” – it’s merely an advanced system that is trained, not told. AI mimics the human brain in the way that it learns. It starts with no information, and after being given thousands of pieces of information, is able to understand and make predictions about data it has never seen before.
AI will become a threat to small businesses if owners believe it won’t impact them, or isn’t already impacting them. The fact is, AI has the potential to drastically help companies of all sizes work smarter and more efficiently than ever before.
Before acting on an AI roll-out, here are the top four questions small businesses should ask themselves:
1 . What is it you are looking to achieve?
AI can provide great value for sales, marketing, finance, HR, customer service, and more. Hone in on what exactly you are hoping to achieve with the use of AI – where do you need to increase productivity?
By setting highly focused goals, you will be able to develop a plan that prioritizes specific applications for AI technology. This way, small businesses can slowly adapt and familiarize themselves with the software, that will, overtime, drastically enhance the bottom line.
The most immediate benefit of AI is that it will provide immense efficiency. There will be less time entering data and more time getting valuable insight to augment decision making. There’s a mass amount of data waiting to be analyzed and AI will guide businesses on how to act.
2. What data is already in a system of record?
You’ll never hear the words “too much data” and “AI” used in the same sentence. AI systems become more accurate and effective as the volume of data increases. The big industry players have been accumulating business intelligence and already moved on to predictive analytics.
The first step in your AI project is to systematize your business . With the widespread adoption of cloud based solutions (SAAS) and the rapid reduction in the cost of storage and processing, the first step is to start instrumenting all elements of your business. Your website, your marketing activities, your sales – including the business that you “win” and “lose.”
Unlike huge, multi-national companies that are able to capture and process peta-bytes of data, small businesses have had access to significantly less data. This is changing with the adoption of cloud-based products and services and the availability of open data sets from governments and other providers. The goal for small business owners is to have the appropriate systems and infrastructure needed to go and analyze data and extract even more business value.
3. What is your ability to explore your business data and understand what’s going on objectively?
If you’re looking at the raw data it’s easy to “torture the data” to get the answer you want to be there – don’t fall victim to this habit.
Your goal is to generate several hypotheses from the data. Examine outliers and the associations between data elements. Be careful not to draw conclusions too early though, as outliers could be caused by “bad data” that needs to be cleaned up, and the relationships may not be strong enough to make any definitive conclusions. We often allow our personal biases and expectations get in the way of looking at data. The numbers don’t lie, but if we look at them expecting certain results, we may end up manipulating the information to meet our expectations. In order to take full advantage of AI, we need to be able to trust the numbers.
You don’t need to use expensive tools; use the reports and dashboards that are built into the tools you already have and approach the problem with an inquisitive mind. Look for the unexpected and when you detect something that’s interesting, create one or more hypothesis to explain what you’re seeing, and then set about to prove or disprove it.
4. Are your technology providers able to support these capabilities to provide more meaningful insights?
AI will not provide any benefit if small businesses lack the IT infrastructure to support it. Start by upgrading your approach to IT – move toward a cloud-based resource that can support AI once implemented. Data is a prerequisite to introducing AI into a system, and a paper system is useless when it comes to incorporating AI.
Make sure your goals are aligned with the direction your software is going. If it doesn’t seem as though your software provider is working toward the same future as you, it might be time to consider another option. It’s important to ensure your provider is taking steps to remain relevant in the future of technology.
If you’re just getting started on the business analytics journey, begin by using the reports and dashboards that your systems have today. Become familiar with the digital assistants that are already on your smartphone; explore what they are already able to do and stay current with how these systems are evolving.
By making an effort to understand and embrace AI, small businesses are optimizing operations, improving customer-service, and growing their bottom line. Imagine where your company would be if you didn’t embrace the uncertainty of the internet or didn’t go mobile in the age of the smartphone. Artificial intelligence is the newest technology adding efficiency and intellect to small business – don’t be late to adapt; be better, faster, smarter operators with the use of AI.
Christine Crandell is the author of this article, that was developed with Kevin Haaland, CTO of The Better Software Company, a SaaS platform to small business and franchise owners.