Revolutionizing Industries with Business AI: The Autom8 Advantage
Business Artificial Intelligence (AI) is no longer a futuristic notion; it’s a present-day reality that Autom8 leverages to transform operations, enhance customer experiences, and ensure regulatory compliance across all platforms and software. As an agnostic AI solutions provider, Autom8 integrates seamlessly with diverse systems, ensuring optimal performance and innovation.
Here’s how Autom8 is leading the AI revolution, from Customer Relationship Management (CRM) to manufacturing and beyond:
Meet MARWIN
MARWIN is a first-of-its-kind AI-driven intelligent bot that can extract unstructured non-personal data from websites, images, and documents. Once Marwin collects the relevant data, his AI engine converts it into structured data, giving our customers unprecedented access to market intelligence and allowing them to make insightful business decisions and stay ahead of the competition.
Where is AI used
Industry
Agriculture
Automotive
Banking and Financial Services
Construction
Education
Energy and Utilities
Entertainment and Media
Food and Beverage
Government and Public Sector
Healthcare and Pharmaceuticals
Hospitality and Tourism
Department
Human Resources
Finance and Accounting
Sales and Business Development
Marketing and Advertising
Customer Service and Support
Operations and Production
Supply Chain and Logistics
Information Technology (IT)
Research and Development (R&D)
Quality Assurance and Control
Procurement and Purchasing
What is Artificial Intelligence (AI)
Does your business have high-volume, low-value, repetitive and mundane tasks? If the answer is yes then your business can benefit from Robotic Process Automation or RPA technology.
However, not all business processes are suited to be automated. One of the biggest mistakes that organisations make when it comes to RPA and automation in general is adding automation to complex processes that do not suit RPA. RPA is designed to automate repetitive, rule-based processes and flag exceptions for human interventions.
Try not to fall into the trap of making an intuitive decision of which processes should be automated. Instead, conduct an objective analysis. A good framework to help you determine if RPA suits a process is the following:
The Four Types of Artificial Intelligence
ARTIFICIAL INTELLIGENCE DEFINED: FOUR TYPES OF APPROACHES
- Thinking humanly: mimicking thought based on the human mind.
- Thinking rationally: mimicking thought based on logical reasoning.
- Acting humanly: acting in a manner that mimics human behavior.
- Acting rationally: acting in a manner that is meant to achieve a particular goal.
Reactive Machines
The most fundamental AI principles are followed by a reactive computer, which, as its name suggests, can only use its intellect to see and respond to the environment in front of it. A reactive machine cannot store a memory and, as a result, cannot rely on past experiences to inform decision making in real-time.
Reactive machines can only perform a small number of highly specialised tasks because they are only capable of experiencing the world immediately. However, intentionally limiting the scope of a reactive machine’s worldview means that this kind of AI will be more dependable and trustworthy – it will respond consistently to the same stimuli.
The chess-playing supercomputer Deep Blue, which was created by IBM in the 1990s and defeated Gary Kasparov in a game, is a well-known example of a reactive machine. Deep Blue was only able to recognise the chess pieces on a board, know how each moves according to the game’s rules, acknowledge each piece’s current position, and decide what would be the most logical move at that precise moment. The machine wasn’t striving to better place its own pieces or anticipate prospective movements from the other player. Every turn was perceived as existing independently of any earlier movements and as having its own reality.
Google’s AlphaGo is another illustration of a reactive machine that plays games. Due to its inability to predict moves in the future and reliance on its own neural network to analyse game developments in the present, AlphaGo has an advantage over Deep Blue in more difficult games. In 2016, champion Go player Lee Sedol was defeated by AlphaGo, which has already defeated other top-tier opponents in the game.
Reactive machine AI can achieve a level of complexity and offer dependability when developed to carry out recurring tasks, despite its constrained scope and difficulty in modification.
Limited Memory AI
When gathering information and assessing options, limited memory AI has the capacity to store earlier facts and forecasts, effectively looking back in time for hints on what might happen next. Reactive machines lack the complexity and potential that limited memory AI offers.
Limited memory An AI environment is developed so that models can be automatically taught and refreshed, or AI is created when a team continuously teaches a model in how to understand and use new data.
The following six actions must be taken when using ML with restricted memory AI: The ML model must be developed, be able to generate predictions, be able to accept feedback from humans or the environment, be able to store that feedback as data, and all of these stages must be repeated in a cycle.
The three main ML models that make use of AI with limited memory are:
Reinforcement learning, which gains experience by repeatedly making mistakes and learning from them.
Long short term memory (LSTM), which makes use of historical information to forecast the following item in a sequence. LTSMs devalue data from further in the past while still using it to draw conclusions since they believe it to be more essential when making forecasts.
Evolving over time, generative adversarial networks (E-GAN) expand to explore slightly altered routes based on prior experiences with each new choice. This model continuously seeks a better path and predicts outcomes throughout its evolutionary mutation cycle using simulations, statistics, or chance.
Theory of Mind
Theoretical is exactly what Theory of Mind is. The technological and scientific advancements required to reach this advanced level of AI have not yet been attained.
The idea is founded on the psychological knowledge that one’s own behaviour is influenced by the thoughts and feelings of other living creatures. This would imply that AI computers might understand how people, animals, and other machines feel and make decisions through self-reflection and determination and would use that knowledge to make their own decisions. In order to create a two-way communication between humans and AI, robots essentially need to be able to understand and interpret the concept of “mind,” the fluctuations of emotions in decision making, and a litany of other psychological concepts in real time.
Self-awareness
The last phase in the development of AI will be for it to become self-aware once Theory of Mind has been created, which will likely take a very long time. This sort of AI is conscious on a par with humans and is aware of both its own presence and the presence and emotional states of others. It would be able to comprehend what other people could need based on both what they say to them and how they say it.
AI self-awareness depends on human researchers being able to comprehend the basis of consciousness and then figure out how to reproduce it in machines.
