The Foundations of AI for Business

Although the concept has existed for decades, artificial intelligence (AI) has more recently emerged as a prominent buzzword, capturing the attention of companies across industries. However, as with any trend that gains significant traction, there are challenges and complexities associated with the buzz around AI. While the hype has undeniably brought attention to the potential of AI, it has also led to some misconceptions and inflated expectations. 

To truly harness the power of AI and avoid falling into the pitfalls of jumping blindly onto a bandwagon, it is essential for companies to develop a solid understanding of what AI is, how it functions, and how it can be leveraged to drive meaningful business outcomes. Only by acquiring this foundational knowledge can organizations make informed decisions about integrating AI technologies into their operations and unlocking their transformative potential.

That’s why we’re delving deeper into the world of AI. Here we will explore the underlying concepts and real-world use cases, providing valuable insights to help you better grasp the power of AI and form a deeper understanding of how it can be leveraged to meet your business goals.

What is AI? 

At its core, the term ‘AI’ refers to the development of intelligent machines that can simulate human intelligence, enabling them to perform tasks that typically require human cognitive abilities, such as problem-solving, decision-making, and learning. According to the father of AI, John McCarthy, it is “the science and engineering of making intelligent machines, especially intelligent computer programs”. Although today we are discussing the foundations of AI for business, AI lives within the intersection of many classic disciplines, including philosophy, neuroscience, behavioral economics, computer science, and mechanical engineering.

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The Goals of AI 

The researchers and developers working on AI do so with two main goals: creating expert systems and implementing human intelligence in machines.

Create Expert Systems

Expert systems are systems which are designed to mimic the knowledge and decision-making abilities of human experts in specific domains. These systems leverage AI techniques to exhibit intelligent behavior, learn from data and experiences, demonstrate expertise, and provide advice to users. Expert systems are typically built using rule-based approaches, where a set of predefined rules and knowledge is used to make informed recommendations or solve complex problems. They are widely used in fields like medicine, finance, and engineering to assist professionals in making accurate and reliable decisions.

Implement Human Intelligence in Machines

This goal of AI revolves around creating systems that can understand, think, learn, and behave like humans. It aims to replicate the cognitive abilities and processes of human intelligence in machines. These systems may use techniques such as machine learning, natural language processing, and knowledge representation to simulate human cognitive processes. By implementing human intelligence in machines, AI aims to enhance human-computer interactions, enable machines to understand user needs, and perform tasks that were traditionally exclusive to humans.

Achieving these goals requires a combination of various AI techniques, including machine learning, knowledge representation, reasoning, and natural language processing. Researchers and developers are continuously working towards improving the capabilities of AI systems to learn, demonstrate expertise, understand complex concepts, and provide meaningful insights and recommendations. While AI systems may not fully replicate all aspects of human intelligence, advancements in AI continue to push the boundaries, bringing us closer to machines that can exhibit increasingly sophisticated cognitive abilities.

AI, Machine Learning, and Deep Learning

It’s important to make a clarification at this point, as there are several terms that we often see being used interchangeably: AI, Machine Learning (ML), and Deep Learning. Each of these has a distinct meaning.

AI serves as the broad umbrella term encompassing the overarching goal of creating intelligent machines. It encompasses a range of techniques, algorithms, and methodologies aimed at enabling computers to exhibit human-like intelligence.

Machine Learning (ML) is a subset of AI that focuses on algorithms and models that allow computers to learn from data and make predictions or decisions without being explicitly programmed. ML algorithms are designed to analyze and interpret patterns and structures in data, learning from examples and experiences to improve performance over time.

Deep Learning, on the other hand, is a specific subset of ML that uses artificial neural networks inspired by the structure and function of the human brain. Deep Learning algorithms are designed to automatically learn hierarchical representations of data, uncover intricate patterns, and extract high-level features by analyzing large amounts of labeled data.

5 Distinct Research Areas

Within the AI field, there are five distinct research areas that have gained significant attention and have had a profound impact on the development and application of AI technologies. These areas include Machine Learning (ML), Expert Systems, Computer Vision, Natural Language Processing (NLP), and Robotics. Each area focuses on unique aspects of AI, addressing specific challenges and exploring different techniques.

Machine Learning

ML, a fundamental sub-discipline of AI, empowers machines to make autonomous decisions by learning and interpreting patterns in various data types. It involves four types of learning:

1. Supervised learning: Using labeled data, predict the best estimate of an output variable given new input data. For example, predicting sales based on features like week of the year and price.

2. Unsupervised learning: Discover hidden relationships and anomalies within data without labeled examples. For instance, uncovering patterns in demographic information and ZIP codes.

3. Semi-supervised learning: With limited labeled data, estimate missing labels for an input dataset.

4. Reinforcement learning: Train an artificial agent to maximize utility based on a defined objective and user-defined utility function.

5. Supervised learning is widely used in business, but other learning methods offer promising potential for emerging use cases.

Expert Systems 

An expert system (ES) is an artificial agent that uses pre-programmed knowledge for decision-making and advice. It operates on the basis of rules, resembling a complex decision tree or nested 'if-then' logic. While expert systems may not receive the same attention as machine learning (ML), they offer several advantages. They can incorporate human insights gained from trial and error, ensuring valuable expertise is captured. Expert systems are more predictable and less prone to extreme errors with novel inputs. Additionally, they have traditionally been faster and easier to implement, although ML has become increasingly accessible. Expert systems are commonly used in conjunction with ML to mitigate the risk of inaccurate predictions.

Computer Vision

Computer vision (CV) is the automated analysis and interpretation of images or videos, converting them into numerical arrays. This enables ML algorithms to make predictions and generate new images based on user inputs. Recent advancements have fueled CV's growth:

·  Efficient algorithms: Deep learning, particularly convolutional neural networks, has reduced memory usage and computation time for CV tasks.

·  Improved computing resources: GPU enhancements, distributed architectures like Spark, and affordable cloud computing have made memory-intensive CV algorithms more accessible.

·  Abundance of training images: The rise of social media, online communities, and digital cameras has exponentially increased the availability of publicly accessible images for training CV algorithms.

These innovations have unlocked numerous CV applications, such as self-driving cars and automated retailers like Amazon Go. As the cost of cameras and spatial sensors decreases, we can expect even more use cases to emerge.

Natural Language Processing (NLP)

NLP involves enabling machines to understand and generate human language, automating language extraction, analysis, and generation. It parses sentences to infer meaning and intent through techniques like word/letter splitting and bidirectional reading. NLP has diverse applications, including named entity recognition, part-of-speech tagging, question answering, machine translation, text summarization, spellcheck, and autocomplete. Deep learning innovations have accelerated NLP progress, facilitating faster training of ML models on human language. Virtual assistants like Siri and Alexa exemplify NLP's capabilities, allowing hands-free control over music, web search, to-do lists, and smart-home devices. As these assistants collect user data, they will improve, unlocking new use cases and integration with enterprises.

Robotics

Robotics combines AI with physical systems to design, operate, and apply robots to solve human problems. It’s difficult to define the term ‘robot’ precisely, and the breakneck speed at which the robotics field advances means we’re constantly seeing new designs and use cases that seem to have come straight out of a science fiction novel. Today, most robots rely on expert systems to accomplish their objective, but the robots of tomorrow will become exponentially more useful by incorporating machine learning, computer vision, and natural language processing into their operating systems.

Developing the Foundations of AI in Your Business

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Now that you have a little more background on the foundations of AI, you’re likely thinking about how to leverage AI in your organization to increase efficiency and automate processes. Our next blog post explores the value of AI for businesses and outlines how to set yourself up for success before you even begin your AI journey. If you have questions about the use of AI in your organization, get in touch with us today. The data experts at Fastloop will work with you to unlock the full power of your data through AI, creating real impact for your organization now and into the future.

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By Yogesh Bhatt, Chief Data Officer‍

Yogesh leads all technical aspects of Fastloop, including client strategy, technical leadership and all data, analytics, AI and digital transformation services. Yogesh brings a wealth of global experience to Fastloop, including ~25 years working with some of the globe’s largest data and AI programs across sectors including Oil & Gas, Retail, Loyalty, Airlines, Automotive, Heavy Industry, Manufacturing, and CPG among others.  Prior to Fastloop, Yogesh spent much of his career managing Data, Cloud, Analytics and ML/AI services across 14 countries for global consulting firms.

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