What is Artificial Intelligence  (AI)?

In plain and simple terms, Artificial intelligence (AI) also called Machine Intelligence (MI) leverages computers and machines that aims to emulate the problem-solving decision making decisions modalities like that of a human.

What is an Artificial Intelligence (AI) Model?

An AI is system built by developing AI models. An AI model are stochastic mathematical and statistical algorithms that collects, processes, analyzes, and converts data into predictive and prescriptive decision support systems to solve specific real-world problems.

What are the different types of AI?

There are 2 types of AI;

  1. Weak AI or Narrow AI

This is also called Narrow AI or Artificial Narrow Intelligence (ANI) —is AI trained and focused to perform specific tasks. These days weak AI is ubiquitous around us. From our smart phones to Amazon Alexa, Google Siri, Acumentica Alina, and autonomous vehicles are some robust solutions built around weak AI or Narrow AI as we prefer to call it.

  1. Strong AI or AGI (Artificial General Intelligence)

Artificial General Intelligence (AGI) in theory is a machine which would have intelligence synonymous to that of a human being. It would be consciously aware of its surroundings and will have to ability learn, plan, and solve problems without any teaching.  The movie the “Terminator” with Arnold Schwarzenegger is example of machine with super intelligence, far exceeding that of a human brain. Though AGI is still currently in its theoretical phase, it’s doesn’t mean AI researchers and enthusiasts are not exploring it’s development.

Difference between Machine learning (ML) vs. Deep Learning (DL)

We often here deep learning and machine learning being used interchangeably, but it’s important to understand the different between the two. To begin with, both deep learning and machine learning are sub-fields of AI of which deep learning is an actual subset of machine learning (ML).

Machine Learning  

As aforementioned above, Machine Learning (ML) is a subfield of AI and refers to technologies and algorithms that enable machines to recognize patterns and blind spots, perform decisions, recommendation support functions , and self learns and improve themselves over time. Types of Machine Learning The 3 types of Machine Learning are:

  1. Unsupervised learning

Unsupervised learning takes it to the next level as it uses unlabeled data. The system is given the flexibility to identify patterns and associations, often generating inferences that might have been a blind spot to a data scientist or data analyst. A common use for unsupervised learning is in the area of clustering . Clustering is the process of  identifying similarities in  data points and group similar data points together which help profile the attributes of different groups. An example of where un-supervised learning is used in the Shopping/e-commerce websites and recommending movies such with NetFlix. The system use machine learning algorithms to decide what recommendations to make to specific users based on their past purchases or what genres of movies to recommend  based on what they have watched.

  1. Supervised learning and Semi-supervised learning

Supervised learning is a subset of machine learning that requires human interventions most of the time. The system is fed training metadata through an optimized machine model explicitly designed to teach and learn it how to respond to the metadata. An AI computer science programmer and/or a data scientist will then validate the inferences, predictions and simulation of the run to issue any kind of corrective action for any in accurate responses.  An example of this is teaching the system to recognize the image of an apple. This is a classification problem whereby the system must know that this is an apple based on the image. The system is trained to recognize this image as well as all other images. Every output is confirmed by the data scientist. Over time once trained, the supervised machine learning agent is accurately able to validate new datasets e.g. fruits that follow the learned patterns. Semi-supervised learning is the combination of supervised and unsupervised machine learning. It uses more un labelled data than labelled data and searches for patterns automatically. Here the machine model is trained to label data without having large training labelled datasets.

  1. Reinforcement learning

In supervised and unsupervised learning, there is no consequences if a system fails to accurately understand or categorize data. However what if, like a child receives a reward (a star) when it does something right and not receive a reward when it does something wrong (no star). In case of an AI agent, it’s receives a score when it does something right and a negative score when it does something wrong. Over time, through trial and error the system would automatically learn how to get the tasks done, reinforcing good actions and results. Reinforcement learning systems machines help solve complex tasks and challenges for a given business because it has to with a velocity of copious unpredictable metadata sets. In our opinion reinforcement learning to closest to a human learning modality.

Where is Machine Learning used today?

  1. Heath care – Implementing dynamic treatment regime (DTR’s) for patients suffering from long-term illnesses.
  2. Autonomous cars – Telsa and Waymo
  3. Traffic light control
  4. Sales  – Acumentica AI Customer Generating System
  5. Stock Predictions – Acumentica  AI Stock Predictive System

and more.. In addition to the above areas where Machine Learning (ML) is used, it might come as a surprise to you that we interact with machine learning systems and tools in our everyday life.

  • Google – Filtering and removing spam from your inbox uses machine learning.
  • Banks – Identifying suspicious malicious activities  uses machine learning.
  • Amazon Alexa – Voice recognition uses machine learning.

and more. Company’s today are building all sorts to robust applications that solves specific challenges in all industries and verticals.

What is Deep learning?

Deep learning is a branch of machine learning that uses neural networks comprised of many stratums. The difference between machine learning and deep learning has to do with the modality of each algorithm. In machine learning, the agent is given a set of mined data to process and analyze. In deep learning, it’s provided raw data and the agents self-determines what data is relevant or not- eliminating some of  the human intervention required and enabling the use of plethora data sets whether it be structured or unstructured data. Deep learning neural networks gets more intelligent over time as the amount of data used to train the system increases. So deep learning can leverage labelled, structured datasets like with supervised learning to teach the agent but is does necessarily require a label dataset. It can use raw un-labeled unstructured data to autonomously distinguish and process the different hierarchy data categories. This allows us to scale and use machine learning to solve problems and provide solutions in many interesting facets. Where is Deep Learning used today?

  1. Fraud Detection
  2. Natural Language Processing (NLP ) – Acumentica AI Growth System
  3. Customer Relationship Management  – Acumentica AI Customer Generating System
  4. Stock Predictions  – Acumentica AI Stock Predictive System
  5. Computer Vision
  6. Agriculture
  7. AI – Acumentica AI Voice Recognition System
  8. Virtual Assistants
  9. E-commerce
  10. Manufacturing

And more.. How does AI work? Think of about the petroleum used to run a vehicle. In order for that to happen, oil is first fracted from the earth. That oil then goes through a refining process to remove oil impurities.  Once the oil impurities are removed and cleaned (through the refinery system),  it then gets transported to the gas stations for usage. The same analogy applies to how AI works.  Data (structured data and/or unstructured data) is first collected. The data is then processed and analyzed, removing what we called “outliers”. Once you have good, clean actionable data, the good data is fed into an AI system which contain intelligent learning algorithms that utilizes the good training data to self-learn and solve problems with limited no human intervention. It’s important to pontificate that Big data plays a important role in the efficacy of inference in all AI systems. Why Acumentica?  Your GROWTH is our success. This is our DNA. We are relationship based and our product and services are around growing a company by increasing revenue, ROI, and cost reduction. We add value and help your business by.

  • Understanding your business pains and challenges.
  • Provide the right CXO Executive skills and AI Intelligent solutions to meet your goals.
  • Garnering recommendations/solutions that is specifically tailored to your needs.
  • Partnering and working alongside with you to achieve your company goals.

Acumentica is an Artificial Intelligence (AI) software and CXO analytics services company. Our flagship product is our AI Growth System who knows all about your next customer, competitors, markets, and business in real-time. Our Flagship products will be able to.

  • Increase sales
  • Increase demand awareness
  • Increase ROI

Listed below are our products and their value.

  • AI Customer Growth System– Autonomously generate qualified customers at your fingertips in real-time.
  • AI Digital Growth System– Autonomously in real-time, increase your customer acquisition rate organically.
    • AI SEO System– Autonomously increase your conversion rate and page rank organically.
    • AI Map Optimizer System– Autonomously in real-time, increase your page rank and conversion rate organically without paid ads.
    • AI Social Media System– Autonomously, in real-time increase revenue and market share by identifying quality leads and gaining competitive edge against your competition.
  • AI Market Growth System – increase market share and gain competitive edge at your fingertips in real-time.

Acumentica gives enterprises the AI tools they need to transform their business systems while significantly lowering costs . For more information on how Acumentica can help you complete your AI journey, Contact Us or  explore Acumentica AI Growth Systems.