Artificial Intelligence, or AI, is the theory and development of computers who are able to perform tasks that would normally require the input of a human. Artificial intelligence is the technology that allows computers to do things that were once only the domain of humans. For example, computers have always been able to calculate but with Artificial Intelligence technologies, computers can learn and draw conclusions automatically.
Artificial Intelligence technology is now being put in the hands of everyone to make better business decisions and is becoming increasingly more apparent in our home and work lives. It was first born in the 1970s alongside the concept of algorithms, however with a lack or quality of Big Data available this made Artificial Intelligence technologies impossible to implement.
Fast forward to 2012, there was now a significant amount more of Big Data and the beginning of the Cloud era triggered the growth of Artificial Intelligence technologies. For Artificial Intelligence to work within your organisation specific data is necessary to get the required business outcome.
From 2019, the United Kingdom is planning a six-fold investment in Artificial Intelligence technologies, pledging more than £1bn in the set up and deployment of these technologies.
Artificial Intelligence amplifies human ingenuity, and these technologies can learn and form conclusions as well as being able to interpret the meaning of data. For example, movie-streaming websites suggest movies, TV shows and documentaries similar to those you have seen and rated positively. Other aspects like the time of day and also which day it is are also taken into consideration, so you receive better content based on your consumption habits. These websites are constantly getting “smarter” as their databases continue to expand. It is this kind of large database analysis and processing is called Big Data.
Big Data is a term that describes a volume of data which is both structured and unstructured. Artificial Intelligence doesn’t work without data and Big Data is so called because organisations are harvesting enormous pieces of information it takes in. There is simply a huge amount of data available of all types which includes images, audio, and text.
Every time you fill out a form or make a decision that’s recorded by a computer the information you provide is potentially added to a Big Data set. Data can also come from the IoT, internet-based transactions and other sources.
The larger, more specific and better quality of the sample size of Big Data, the more it gives AI the ability to be smarter, to sound more human, to more accurately predict future decisions as well as revealing patterns, trends and the required business output. On the contrary if you feed your AI with small amounts, non-specific and poor-quality data, you’ll soon reap bad and biased results.
For example, Transport for London (TfL) is gathering data via WiFi beacons from its passengers each day throughout its London Underground stations. This is being done to better understand its customers journeys, better manage unexpected events and avoid congestion at peak times by sending its customers an alert on their phone to suggest an alternative route. By examining how their customers move throughout the network, Artificial Intelligence can therefore be used to highlight footfall which in turn TfL can strategically price advertising space in accordance with the footfall from its customers.
Artificial Intelligence can be roughly divided into two divisions, Machine Learning and Deep Learning.
Machine Learning (ML), as the name suggests, empowers machines to learn by themselves using the data provided to make accurate predictions. By training algorithms, computers are able to learn to make decisions. Machine Learning is actually a subset of Artificial Intelligence technology and is a subset of realising AI.
Moving on further, Deep Learning (DL) is a subset to Machine Learning and simply put is the next evolution of Machine Learning. DL algorithms are somewhat inspired by the information processing patterns that are found in the human brain. Humans use their brains to identify patterns and to classify pieces various pieces of information and DL algorithms can be taught to accomplish the same tasks as machines.
For example, DL can automatically discover the features to be used for classification by using considerably large amounts of training data to deliver accurate results whereas ML requires these features to be provided manually.
The focus of the EU’s General Data Protection Regulation (GDPR) is on data processing of personal data, especially large amounts of data. GDPR has had a tremendous impact on how organisations of all size do business.
The relationship between AI and GDPR is a multidimensional one. While AI can effectively help detect GDPR violations, there are aspects of the regulation where While organisations of all sizes are gravitating towards implementing AI by employing Big Data collection and analytics to turn input into quantifiable statements and actionable business plans. However, the ease of implementation and deployment of AI has hit the roadblock of GDPR.
As previously discussed with the large samples size of Big Data required for Artificial Intelligence, causes problems when it comes to adhering to GDPR. Artificial Intelligence can provide the rapid detection of data intrusions and removes human error, there are however still issues around right to explanation and the obscurity of understanding AI decision making.
Although AI has the potential to be a solution to tackling data security issues, it also only takes into consideration the data it is fed. This means that Machine Learning will not automatically comply with GDPR unless it is clearly programmed to with human input and reasoning.
Within the Microsoft 365 platform, AI is already integrated into the applications your organisation is already familiar with. These Microsoft applications help amplify your employees’ skills, promote teamwork and uncover hidden insights to make better business decisions. Microsoft 365 also helps to proactively manage external threats to protect sensitive business and personal data.
Microsoft 365 provides insightful search functions which harvest knowledge and take action faster, these are also personalised to your organisation’s network. The search functions within Microsoft 365 are boundary-less – meaning that search is performed across multiple devices, data stores and services.
Artificial Intelligence enables your employees to present more inclusively with live captions and subtitles in Microsoft PowerPoint and in the web version of Outlook, the new features give Microsoft 365 users meeting insights, suggested replies with a meeting, smart time suggestions, and suggested locations. Outlook will tap into Microsoft Graph to recommend information that it thinks will be useful for your employees next meeting, as well as sorting information about previous meetings, such as files shared via email or stored in Microsoft SharePoint and OneDrive. In addition, AI can be integrated with Microsoft Access to streamline user experience.
Microsoft Dynamics 365 AI for Sales has Artificial Intelligence built in to help your sales team create personalised engagement and proactive decision making to build better relationships and increase revenue.
Azure Search is the only Cloud based search engine with built-in Artificial Intelligence technology. Azure’s AI search can easily and quickly identify and explore relevant content at scale and has the capabilities to read typed, scanned and handwritten text as well as having facial recognition features. These features are beneficial for paper-based organisations such as law firms and finance departments who are looking to migrate their data to the Cloud.
Despite the rapid rate of which technology is progressing, Artificial Intelligence is actually only in its early stages of development. The more Big Data that is available from organisations to feed into AI, the smarter the applications will become.
The demand for revolutionary ways to store the amount of digital data is growing exponentially, but the capacity of current data storage media solutions cannot keep up with current demand. Most of the data storage used today is stored on magnetic or optical media. Despite significant improvements in optical disks; being able to store 1 zettabyte of data, or 1000000000 terabytes, would take up a large amount of physical space as well as many millions of units of data storage devices.
Microsoft are currently working with the University of Washington on Project Palix which allows binary code to be written and then synthesised into manufactured DNA storage. This revolutionary DNA data storage is 1,000 times denser than tape data storage and can last 2,000 years at 10 degrees Celsius.