Benefits of AI

Some of the benefits of AI include firstly, greater efficiency. AI brings a new level of efficiency to the use of resources. Machine learning can extract meaning from large and complex data sets. AI can therefore see patterns and anomalies in data that humans cannot. Consider cancer detection, for example. Human specialists can recognise several hundred malignant patterns in a cancer scan, whereas AI can recognise thousands. 

Secondly, great accuracy. In recent years, AI has been able to analyse complex weather patterns and climate data, which can result in more accurate climate forecasts and, consequently, in the prevention of natural disasters, such as tornadoes and hurricanes. This is important, as it means that we can be aware of natural disasters coming and have more time to respond or minimise their impact.

Thirdly, better experiences for customers. Using AI, a business can improve the way it interacts with its customers. This could involve things like customer experience through chat bots and digital assistants, who are available 24/7 to converse with customers. It could also mean that in hospitals, for example, AI can focus on the manual and repetitive tasks such as understanding a patient's medical history by reading through all the historical records, while nurses focus on a more human side of their jobs, like forming close interpersonal relationships with patients. AI has also led to other really fun and innovative solutions.

In certain hotels, using an app, hotel guests are now able to check-in to their hotel simply by using their fingerprints or by taking a selfie.These are hugely impressive strides forward. However, it should not be interpreted to mean that human expertise will no longer be needed. The very best results are still achieved when human experts work hand in hand with AI, each bringing the best of their unique capabilities to a problem.

  

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Artificial Intelligence vs Automation

The terms Artificial Intelligence and Automation are frequently used interchangeably. They both relate to software, physical robots, and other machines that allow us to be more efficient and effective. However the complexity levels of the two systems are very different.

Automation is making something run by itself with little or no interaction from humans by following patterns and rules to perform repetitive tasks.

Automation is everywhere and is used in everyday life without you even realising it. When you receive a reminder email or text message for a specific appointment, this will most likely be an automated response, set up in a system beforehand. For example, when you book an appointment with the dentist and receive an email before your appointment, this is likely to be an automated response. This has made work simpler and easier for businesses, whilst offering its customers a more personalised experience. It has also allowed staff to have more time to dedicate to more interpersonal tasks, such as talking to customers and understanding their problems, as the forgetful and repetitive tasks (such as sending out a reminder email), are already taken care of!

Artificial Intelligence is used to assist humans in non-repetitive tasks to find patterns, learn from experiences and then using machine learning choose the correct responses. Unlike Automation, it does not follow orders or rules.

Artificial Intelligence is used to provide insights. For example, imagine someone has a head injury and needs to determine the level of damage. An AI machine could help diagnose the degree of damage by being ‘trained’ on multiple X-rays of previous head injuries. It would then understand the severity of the current head injury and provide an informed result. This could help doctors provide their overall diagnosis for the patient a lot quicker, as they would already have gained the insight on the severity of the injury from the AI machine and would therefore have a better understanding of the patient’s condition before doing their checks

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Behind the scenes of AI: Act and Learn

This is about enabling a machine to take decisions in a physical or digital world based on the AI's understanding of what is required. Through this process, the AI can continuously learn, improve the outcomes, and become better at doing the task. It can continuously optimise the performance by learning from successes or failures of these actions. Some examples of where AI is acting and learning are-- Netflix, suggesting movies, TV shows, and documentaries, based on a viewer's prior activity, patterns, and behaviours. The more you watch, the more it learns, and better suggests relevant content for you to watch. Self-driving cars-- deep learning is one of the things that makes self-driving cars possible.

Key Words

Deep Learning - The visual representation of data on a report or dashboard .

Based on Forrester’s analysis, here’s my list of the 10 hottest AI technologies:

1. Natural Language Generation: Producing text from computer data. Currently used in customer service, report generation, and summarizing business intelligence insights. Sample vendors: Attivio, Automated Insights, Cambridge Semantics, Digital Reasoning, Lucidworks, Narrative Science, SAS, Yseop.
2. Speech Recognition: Transcribe and transform human speech into format useful for computer applications. Currently used in interactive voice response systems and mobile applications. Sample vendors: NICE, Nuance Communications, OpenText, Verint Systems.
3. Virtual Agents: “The current darling of the media,” says Forrester (I believe they refer to my evolving relationships with Alexa), from simple chatbots to advanced systems that can network with humans. Currently used in customer service and support and as a smart home manager. Sample vendors: Amazon, Apple, Artificial Solutions, Assist AI, Creative Virtual, Google, IBM, IPsoft, Microsoft, Satisfi.
4. Machine Learning Platforms: Providing algorithms, APIs, development and training toolkits, data, as well as computing power to design, train, and deploy models into applications, processes, and other machines. Currently used in a wide range of enterprise applications, mostly `involving prediction or classification. Sample vendors: Amazon, Fractal Analytics, Google, H2O.ai, Microsoft, SAS, Skytree.
5. AI-optimized Hardware: Graphics processing units (GPU) and appliances specifically designed and architected to efficiently run AI-oriented computational jobs. Currently primarily making a difference in deep learning applications. Sample vendors: Alluviate, Cray, Google, IBM, Intel, Nvidia.
6. Decision Management: Engines that insert rules and logic into AI systems and used for initial setup/training and ongoing maintenance and tuning. A mature technology, it is used in a wide variety of enterprise applications, assisting in or performing automated decision-making. Sample vendors: Advanced Systems Concepts, Informatica, Maana, Pegasystems, UiPath.
7. Deep Learning Platforms: A special type of machine learning consisting of artificial neural networks with multiple abstraction layers. Currently primarily used in pattern recognition and classification applications supported by very large data sets. Sample vendors: Deep Instinct, Ersatz Labs, Fluid AI, MathWorks, Peltarion, Saffron Technology, Sentient Technologies.
8. Biometrics: Enable more natural interactions between humans and machines, including but not limited to image and touch recognition, speech, and body language. Currently used primarily in market research. Sample vendors: 3VR, Affectiva, Agnitio, FaceFirst, Sensory, Synqera, Tahzoo.
9. Robotic Process Automation: Using scripts and other methods to automate human action to support efficient business processes. Currently used where it’s too expensive or inefficient for humans to execute a task or a process. Sample vendors: Advanced Systems Concepts, Automation Anywhere, Blue Prism, UiPath, WorkFusion.
10. Text Analytics and NLP: Natural language processing (NLP) uses and supports text analytics by facilitating the understanding of sentence structure and meaning, sentiment, and intent through statistical and machine learning methods. Currently used in fraud detection and security, a wide range of automated assistants, and applications for mining unstructured data. Sample vendors: Basis Technology, Coveo, Expert System, Indico, Knime, Lexalytics, Linguamatics, Mindbreeze, Sinequa, Stratifyd, Synapsify.

 

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Behind the scenes of AI: Comprehend

AI enables a machine to understand the information it collects through pattern recognition, such as finding patterns in social media posts on fraudulent behaviours for insurance claims. This is very similar to how humans interpret information by understanding the patterns presented and their contexts. Some of the technologies that are behind it are-- Natural Language Processing, or NLP. This allows computer programmes to understand spoken language. NLP currently works through a process called deep learning. In essence, language is broken down into shorter elemental pieces in order to teach the machine to understand their relationships and how they work together. Knowledge representation.

This is about representing information about the world in a form that a computer system can utilise to solve complex tasks such as diagnosing a medical condition or having a dialogue in a natural language. Speech recognition. This is the translation of speech into text or format that machines can read. For instance, think about automated phone systems that recognise your voice, process your request, and put you through to the correct department when you call a company. A couple of other examples of machines trying to comprehend are-- Facebook using natural language processing to look for patterns and user posts in order to understand how people feel about a certain brand or product. Chat bots like IP Soft Amelia or IBM Watson performing service desk roles such as handling customer complaints or solving customer help desk issues.

 

Key Words:

<sub>Natural Processing Language (NLP) - The ability of computer solutions to understand and interpret human language naturally.

Knowledge Representation - Representing information about the world in a form that a computer system can use to solve a complex task.

Speech Recognition - The use of sound pattern recognition to understand if two voices are the same.

Pattern Recognition - Pattern recognition is the automated recognition of patterns or irregularities in data.

Chatbots - A computer program designed to simulate an intelligent conversation on a text or verbal basis.</sub>
   

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Behind the scenes of AI: Sense

We like to think about artificial intelligence as a set of technologies that allows machines and systems to sense, comprehend, act, and learn. Let's talk in a little bit more detail about these capabilities and the technologies behind them. Let's start with the first capability, sense. When we talk about sense, we mean that AI lets a machine perceive the world around it by gathering and processing images, sounds, speech, and text. Examples of this include facial recognition, such as when your phone unlocks with a simple glance, Image categorization-- is it a dog or a cat? Sound pattern recognition-- is it fireworks or a bomb going off?

Translating speech to texts-- creating subtitles for movies. Some of the technologies that are behind it are computer vision. This allows machines such as computers of mobile phones to see their surroundings. Computer vision has already made its way to our mobile phones via different e-commerce or camera apps. Audio processing-- this has to do with detecting and translating audio signals.

I will give you two examples related to the sense capability that you might find easy to identify with. The first, Google Cloud Vision, which classifies images into thousands of categories such as sailboats and detects objects and faces within images. The second, Amazon Echo, which acts as a personal DJ that you can control through your voice.

Key Words:

_{Facial Recognition- Technology that can identify a person from an image or video using facial characteristics.
Image Categorisation- The process of putting images into different categories for use within a training model.
Sound Pattern Recognition - The process of classifying sounds into different categories and recognising patterns in the sounds.
Translating Speech to Text - When technology turns language/voice that is spoken into a textual transcript.
Computer Vision - The ability to allow computers to see, recognise and process images in the way that humans can through video or image analytics.
Audio Processing - The analysing of audio signals.}

Tomorrow we'll discuss about the second capability comprehend

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