Is Artificial Intelligence Ready For Your Organisation?
Uploaded on 2019-06-19 in TECHNOLOGY--Developments, TECHNOLOGY-Key Areas-Artificial Intelligence, FREE TO VIEW
A tipping point is approaching where AI will become a full-on commercial tool as AI analysis becomes more common for all businesses to use. It is becoming obvious that this is just the beginning of the way AI will be used, in the future.Artificial Intelligence (AI) is being used by numerous companies in the US for smaller projects and this process is increasing, however, AI will progress into many different areas of commercial operations and this is just the beginning of a much larger AI effect.
Right now, AI is being employed with focused hardware and software projects. These processes improve a business’s views on its current work and future by employing predictive analysis. You can use this AI analyse for our current operations and often the processes can currently be used to improve client interaction. It is also being used to understand and analyse potential future relationships with current and new clients.
This process particularly current works well in providing research on customer and client interactions and ways to improve the business relations with its current and potential new clients.
AI is becoming a practical tool with real-world applications particularly in such areas as predictive analytics, machine learning, natural language processing, voice recognition and response. Technologies such as Natural Language Generation (NLG) are especially important in the enterprise, helping workers automate and improve repetitive and routine jobs and it will gradually enhance crucial business processes. NLG utilises AI to better understand what people want to communicate, highlight what is essential, most important and critical, and then deliver the results in natural language.
Businesses Must Monitor Time-lines And ROI of AI
More and more organisations are deploying AI-powered technologies, with goals such as improving worker productivity and enhancing the customer experience that are not only laudable, but achievable. A focus on realistic deployment timeframes and accurately measuring the effectiveness and ROI of AI is critical to keeping the current momentum around the technology moving forward.
Business Sould Frame AI Adoption As Step Towards Humanising Technology.
Critics often fear the dehumanising effects of new technologies, replacing rich human communications with cold, sterile machine interactions. With AI, the impact can be just the opposite. Technologies like voice recognition and natural language generation can improve how we interact with technology and machines, making it both more natural and more powerful, delivering interactions and yielding insights above and beyond what has been possible up to now. That is the promise of AI, and one that is increasingly within the reach of a growing number of organisations.
The Technologies of Artificial Intelligence
There are many key technologies used in fielding AI. These are the components of AI technologies we recommend tracking:
Machine Learning: Machine Learning is a subset of AI that focused on giving computers the ability to learn without being explicitly programmed. Machine Learning involves the automated training and fitting models to data. ML is the most widely used AI related technology and is frequently the front end of more complex solutions. This is a broad technique with many methods. Methods commonly taught and applied in ML solutions all have different strengths and weaknesses and part of the art of ML is knowing which applies to the need at hand.
Neural Networks: Considered a more complex form of Machine Learning, this approach uses data flow mappings similar to artificial “neurons” to weigh inputs and related them to outputs. This approach views problems in terms of inputs, outputs and variables that associated inputs with outputs.
Deep Learning: Highly evolved neural networks with many layers of variables and features. Important to most modern image and voice recognitions and for extracting meaning from text. Deep learning models use a technique called “back propagation“ to optimise the models that predict or classify outputs, which adds to complexity of the end model. The end model may have so many 1000’s of variables that no human can really understand how the model functions or how a conclusion was arrived at.
Natural Language Processing: This class of technology analyzes and understands human speech and text. Used in modern applications of speech recognition including chatbots and intelligent agents. NLP also requires training data, in this case the output is knowledge about how language relates, often referred to as a “knowledge graph” for a particular domain.
Rule-based Expert Systems: This is an older approach to AI solutions. It involves establishing sets of logical rules derived from the way people actually work. Used in many processes where sets can be clearly defined. This was the dominant form of AI in the past and is still around today, but is really just complex programming. Imagine a large number of “if-then” statements in a program, but in this case the rules were built by domain experts.
Robots and Robotics: This is the automation of physical tasks. Primarily used in factory and warehouse tasks but growing use in health care, small businesses and homes. Training data for robots is critically important, but in this case the training data may include location for movement or a wide variety of expected changes in the environment.
Robotic Process Automation: This is the automation of structured digital tasks in the enterprise or factory. This is a highly evolved form of scripting actions. It is a combination of software and workflows built to help automate business processes. RPA is at its best when it provides users with the benefits of other AI capabilities like Machine Learning.
Key Cyber-Security Innovators
The development of AI techniques has seen Artificial Intelligence (AI) start to appear in a lot of different IT products, including in the field of cybersecurity. In the vanguard of cybersecurity, there’s an elite group of innovative companies building AI into products in order to defeat attackers and win customers.
Here is a list of nine recommended key innovators in cyber-security that are using AI to give their products an edge:
Innovations such as AI will continue to emerge and change the way a hitherto stable group of software operates. C
And cyber-security will continue to evolve and the leaders of the industry will change according to their success, or failure, in adopting the new technologies.
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