Artificial Intelligence
Exploring A.I. in enterprise - a brief explainer & introductory implementation guide
Key Takeaways
- Artificial intelligence can be revolutionary to your business & industry if implemented correctly.
- Handling of sensitive information is one of the biggest drawbacks in A.I. as it introduces potential "data leakage" to the service, software, or cloud providers for running and using A.I. tech.
- Many fortune 500 companies are already using artificial intelligence technologies in one form or another in different business units.
- The effectiveness of using artificial intelligence in your organisation may vary depending on your industry, corporate structure, usage strategy, and how it is used or when it is used.
Recommended reads
- General Understanding - The Machine Learning Simplified: A Gentle Introduction to Supervised Learning by Andrew Wolf
- General Understanding - Prediction Machines: The Simple Economics of Artificial Intelligence by Ajay Agrawal, Joshua Gans and Avi Goldfarb
- In Practice (Enterprise) - Artificial Intelligence in Practice: How 50 Successful Companies Used AI and Machine Learning to Solve Problems by Bernard Marr
- In Practice (Trading) - Artificial Intelligence in Financial Markets: Cutting Edge Applications for Risk Management, Portfolio Optimization and Economics (New Developments in Quantitative Trading and Investment) by Christian L. Dunis, Peter W. Middleton, Andreas Karathanasopolous and Konstantinos Theofilatos
Key research terms & terminologies by functional category
- Machine Learning - supervised, unsupervised, and reinforcement learning.
- Deep Learning - convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs).
- Natural Language Processing (NLP) - text classification, sentiment analysis, and language translation.
- Robotics - autonomous robots, humanoid robots, and swarm robotics.
- Expert Systems - rule-based systems, case-based reasoning, and fuzzy logic systems.
- Computer Vision - object recognition, image segmentation, and visual tracking.
- Knowledge Representation and Reasoning - ontologies, semantic networks, and inference engines.
- Speech Processing - speaker recognition as well as speech recognition and synthesis.
- Planning and Scheduling - automated planning, resource allocation, and job scheduling.
Enterprise A.I. refers to the use of artificial intelligence (A.I.) technologies within in organisations and enterprise settings to simplify operations and improve both long-term and short-term decision-making. By leveraging A.I., organisations can analyse large amounts of data, automate tasks, and gain insights that can help them improve their products, services, and customer experiences. Enterprise A.I. can also be used in a wide range of applications, such as marketing and sales, customer service, supply chain management, and finance. Examples include using natural language processing to analyse customer feedback and sentiment, using machine learning to optimise pricing and inventory management, and using predictive analytics to forecast financial performance. It is a rapidly growing field that is expected to play an increasingly important role in driving innovation and competitiveness in businesses and organisations in the coming years.
At a high level, A.I. works by training computer programs to learn from data, so they can perform tasks that would normally require human intelligence. Here's a very simple explanation. First, you start with a set of data that you want the computer program to learn from. For example, you might want the program to be able to recognise images of cats. Next, you create a neural network, which is a type of computer program that's inspired by the structure of the human brain. The neural network is made up of layers of interconnected "neurons" that are designed to recognise patterns in the data. Then, you train the neural network on the data by showing it lots of examples of a particular concept or object, and letting it learn from those examples. During training, the neural network adjusts the strength of the connections between its neurons to get better at recognising said concept or object.
Finally, once the neural network has been trained, you can use it to make predictions on new data. For example, you could give it an image that it has never seen before and ask it whether or not it contains the said object. The neural network would analyse the image, recognise patterns in it, and make a prediction based on what it has learned during training. Here are 3 of the most popular fundamental principles in artificial intelligence, namely, machine learning, deep learning, and natural language processing (such as ChatGPT).
Machine learning is a subset of A.I. that involves training models on data to make predictions or classifications. This diagram shows how a machine learning model is trained on a dataset, and then used to make predictions on new data.
Deep learning is a subset of machine learning that involves training deep neural networks to learn complex patterns in data. This diagram shows how a deep neural network is composed of multiple layers of interconnected nodes, and how these layers work together to extract meaningful features from the input data.
Natural language processing (NLP) is a branch of A.I. that involves training models to understand and generate human language. This diagram shows how an NLP model can be used to analyse text, and how it can identify entities, extract meaning, and generate natural language responses. Most A.I. solutions are based of 2 main categories of modes of operation as identified by the INV Research team: correlational and hierarchical.
In correlation-based deep learning, a neural network is trained to find correlations between input features and output variables. The network learns to identify patterns in the input data that are correlated with the output variables, and uses these patterns to make predictions on new data. Examples of correlational A.I. includes "autoencoders" and "correlation clustering", both of which are neural network concepts of deep-learning.
In hierarchical A.I., the system is broken down into a series of sub-problems, with each level of the hierarchy solving just one aspect of the problem. Each level receives input from the previous level, processes it, and then passes the output to the next level. The output of the final level provides the solution to the overall problem. The advantage for this design is that it can complex handle tasks that are often difficult to solve using traditional machine learning techniques such as decision-making in dynamic and uncertain environments or object recognition.
Oftentimes, A.I. can be extremely helpful in cases like automating tedious and repetitive tasks, making predictions based on large datasets, and providing personalized recommendations. These can help businesses improve efficiency, reduce costs, and make better-informed decisions. While it is changing the way how industries operate, there are also a bunch of challenges associated with its adoption. One of the biggest challenges is the lack of transparency and interpretability of A.I. models. As A.I. becomes more complex, it can be difficult to understand how the models are making their predictions. This can lead to mistrust of the technology and limit its adoption. Additionally, bias in data can result in biased A.I. models, which can have negative consequences, such as perpetuating discrimination. Ensuring fairness and avoiding bias in A.I. models is an important challenge that must be addressed.
In essence, A.I. models are trained on data that reflects the biases of society. This can result in discriminatory or unfair outcomes, eroding trust of the company using such technologies. Additionally, due to the nature of how an A.I. operate, it is difficult to achieve 100% data anonymity or to ensure complete privacy throughout the many stages of processing and training, and without knowing how it's done or processes, or whether some parts of data can be masked, it will result in a loss of trust. Such cases, might negatively impact organisations in the healthcare industry, and especially difficult for these organisations to dread when privacy-laws such as HIPAA is involved.
In summary, information bias and lack of transparency in A.I. can make people feel feel uncomfortable or suspicious of the technology due to the privacy and security of their personal information being involved as well as the occasional unfair or discriminatory outcomes which hurts the trust in companies. To build trust in A.I., companies must ensure that their models are transparent, explainable, and not subject to common misconceptions or biases. Equally important, is to use people's data in a way that is ethical and respectful of their privacy, while factoring in their consent (letting them know when, where, and how their data is being used). Some solutions includes adhering to the ISO standards in information systems design and handling, as well as choosing what and how you apply the use of A.I. in the different departments of your business.
A.I. is increasingly being adopted across industries due to its potential to improve business processes, increase efficiency, and provide valuable insights. There are numerous real-world examples of how A.I. is being used and applied in today. Let's see a few examples of how A.I. is increasingly being used across industries.
In healthcare, A.I. is being used to improve medical diagnosis, drug discovery, and patient care. For example, A.I.-powered chatbots can help patients with simple medical queries, while A.I. algorithms can analyse patient data to detect potential health issues. While on the manufacturing side, A.I. can help with production processes optimisation, reducing waste, and improving overall quality control. For example, A.I.-powered robots can perform repetitive tasks with high precision, whereas deep-learning algorithms can predict equipment failures to prevent downtime.
In finance and retail, A.I. can be used to improve fraud detection (such as AML or anti-money laundering), risk assessment, and customer service. For example, A.I. algorithms can analyse financial data to detect anomalies that may indicate fraudulent activity, while chatbots can assist customers with simple financial queries. In more detail, retail A.I. can assist with customer experiences personalisation, inventory management optimisation, as well as improving supply chain efficiency. A real world example can include algorithms that analyses customer data to provide more personalised product recommendations or robots that helps with picking and packing of products in warehouses. Now let's talk about some real use cases by Fortune 500 companies.
- Bing's AI-powered chatbot: As you mentioned, Bing is using A.I. to power its chatbot, which is designed to answer users' questions and provide assistance with search queries. Bing's chatbot uses natural language processing (NLP) to understand users' queries and provide accurate responses.
- Amazon's recommendation engine: Amazon uses A.I. to power its recommendation engine, which suggests products to customers based on their browsing and purchase history. The recommendation engine uses machine learning algorithms to analyse customer data and provide personalized product recommendations.
- Tesla's self-driving cars: Tesla is using A.I. to power its self-driving cars, which are capable of navigating roads and highways autonomously. The cars use computer vision and machine learning algorithms to detect and respond to obstacles in their environment.
- Google's Smart Compose: Google's Smart Compose feature uses A.I. to suggest words and phrases as users type emails in Gmail. The feature uses NLP to understand the context of the email and provide relevant suggestions.
- Facebook's content moderation: Facebook uses A.I. to help moderate content on its platform, using machine learning algorithms to identify and remove harmful or inappropriate content. The algorithms are trained on large datasets of labeled content to improve their accuracy over time.
- Pfizer's AI-powered drug discovery platform: Pfizer is using A.I. to accelerate the drug discovery process, leveraging machine learning algorithms to analyse vast amounts of data and identify promising drug candidates.
- Insilico Medicine's A.I. drug simulation platform: Insilico Medicine is a biotech company that uses A.I. to develop new drugs. Their AI-powered platform can design and synthesize novel compounds, predict their pharmacological properties, and optimise their chemical structures.
- Atomwise's A.I. drug screening platform: Atomwise is a biotech company that uses A.I. to screen potential drug candidates. Their platform can simulate the behavior of molecules in the body, predict their efficacy and toxicity, and identify promising drug candidates.
- JPMorgan's AI-driven trading platform: JPMorgan uses A.I. to power its trading platform, which can analyse market data in real-time and make trading decisions based on predictive models.
- BlackRock's Aladdin platform: BlackRock uses A.I. to power its Aladdin platform, which can analyse large amounts of financial data and provide insights into market trends and risks.
- Sentient Technologies' AI-powered trading platform: Sentient Technologies is a hedge fund that uses A.I. to make trading decisions. Their platform can analyse market data, predict market trends, and execute trades autonomously.
Implementing A.I. in an organisation can be a complex process, but here we've outlined some key steps that offers you a general idea on how to implement it in your organisation from the ground up.
Start by identifying the specific problem or opportunity that you want to address with A.I. What are the business objectives you want to achieve? What data do you need to collect, and what kind of analysis or decision-making do you want to enable with A.I.? What kind of A.I. system do you want to implement for, ChatGPT APIs, Apple's Core ML, TensorFlow, H2O.AI, Google Cloud AI, IBM Watson, or even a custom system built from the ground up in Python?
Implementing A.I. requires a team of experts with a range of skills, including data scientists, software engineers, and domain experts. Determine who will be responsible for leading the implementation and who will be responsible for specific tasks such as data collection, modeling, and deployment.
A.I. models rely on large amounts of high-quality data to train the model. Identify the sources of data, preferrably historical data (in a machine-readable format such as CSV, JSON, Excel, etc.) you already have on hand over the years you have been in business; you will need to establish a process (or S.O.P.) for collecting and preparing the data.
Once you have the data, you can develop the A.I. model. This typically involves creating a prototype model, training it on a subset of the data, and testing it to see how well it performs. You will likely need to iterate on the model several times before you achieve the desired level of performance and accuracy.
Once the model is developed and tested, it needs to be deployed in a production environment. This involves integrating the model into the existing systems and processes and ensuring that it operates smoothly and efficiently.
A.I. models are not static, and they require ongoing monitoring and maintenance to ensure that they continue to perform well over time. This involves monitoring the model's performance, identifying and addressing any issues, and updating the model as needed to reflect changes in the data or the business environment. Note that you will have to draw up a process for how you insert or update the model, for instance, if it's manually done, you'll likely need an S.O.P., or if it's automatic, you will need to create middleware to fetch, clean, and then insert the data into the A.I. software for processing.
Here at Innovilage, we have already built multiple A.I. solutions using customised open source software to process large amounts of geopolitical, financial, as well as global resource distribution datasets, creating unbelievably powerful A.I. models. We then re-processed them to fit our unique industry-leading perspectives to produce our renowned CoreEngine and Aspole Versatile products.
One of the advantages for using a pre-built, pre-filtered, and pre-optimised model is that it has already be professional inspected and cleaned up, producing the highest quality data. Whereas, if this was done in-house, it would require a huge team as well as ample dedication to pull off. With our high-quality, minimum distortion and bias data combined with our industry-leading perspectives and robust yet simple interfaces, you can quickly get the insight your organisation needs at the most affordable prices. It is also incredibly secure and fast, as we've configured our cloud infrastructure to operate at its best while prompting your organisation to remove any personal information as soon as our systems detect that it may pose a risk to user anonymity while still staying true to data democratisation.
Here are some of the use cases for our CoreEngine and Aspole Versatile AIs.
- Individuals & clients - Our data teaches and shows end users a holistic view of the world, helping them prepare themselves to better adapt to the environment as well as knowing when and what to prepare, helping you align to your business goals, as well as educating the consumers on a range of relevant everyday life subject matters.
- Investment - knowing what's truly behind the supply, demand, and economics gives you an edge in making better and more informed decisions when investing.
- Research - with our complete financial and industrial data from CoreEngine Megadata accompanied by Econo+, you are sure to get a curated dataset spanning geopolitics to daily market trends, offering you insights you won't get anywhere else.
- Procurement - full access to our CoreEngine Econo+ software will offer you supplier procurement data aligned to different element categories or industries they belong to. Helping you better procure the products you need and at better prices.
Industrial and enterprise AIs are both areas of artificial intelligence that have revolutionary applications in different domains. Industrial A.I. is concerned with the use of A.I. in an industrial setting, such as manufacturing, energy, transportation, and logistics. Its applications include predictive maintenance, quality control, supply chain management, and safety monitoring. Industrial AIs can help organisations optimise their operations, improve efficiency, and reduce costs.
Enterprise A.I. tech, on the other hand, is concerned with the use of said technologies within businesses and organisations to improve operations and decision-making. Its applications include marketing and sales, customer service, supply chain management, and finance. Enterprise A.I. can help organisations analyse large amounts of data, automate tasks, and gain insights that can help them improve their products, services, and customer experiences.
Both of these AIs can play a powerful role in ensuring your company is on-board the next-gen business, driving industry transformations and furthering innovation as well as overall competitiveness. However, there are also challenges that must be addressed to ensure that it is used in a way that is transparent, fair, and ethical while maximizing privacy and effectiveness of applying it. Overall, A.I. adoption in industry is on the rise, and we can expect to see continued growth as businesses recognise the many benefits that comes with this technology.
