“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.
The new General Data Protection Regulation (GDPR) determines how your business does business from 25th May 2018. There are big changes on the way. Your business will need to manage, administer and protect personal data whether you work in B2B or B2C marketing.
The application of GDPR is highly fact-specific. We encourage all organizations using this GDPR Detailed Assessment to contact Infovinity for a free Consultation to discuss GDPR, how it applies specifically to your organization, and how best to ensure compliance.
To help you prepare we have developed this GDPR Assessment/ checklist based on the latest information available. Use it to assess your business and find out which areas you need to focus on. This GDPR Detailed Assessment is intended to assist organizations with assessing their GDPR compliance progress. We hope the GDPR Detailed Assessment identifies technologies and additional steps that organizations can implement to simplify their GDPR compliance efforts.