WHAT IS GENERATIVE AI?
“Unlike traditional computing systems that require careful instructions, generative AI excels in freeform input that results in creative output. Generative AI systems can craft original content like audio, visual art, and text. This proficiency arises from a vast array of training data, which provides it the facility to manufacture human-like behavior.”
WHY DOES THIS MATTER TO MY BUSINESS?
McKinsey recently projected that generative AI will contribute $2.6-$4.4 trillion annually to the global economy by 2040. Important sectors like manufacturing, pharmaceuticals, retail, defense and financial services are expected to be transformed by these technologies.
The use of generative AI will soon extend across nearly every realm of human endeavor, encompassing artistic expression, product design, software deployment, defense systems and drug discovery and healthcare outcomes. Trillions of dollars in actual business value will be won – or lost – depending on how rapidly you are able to gain traction using AI systems. Today, nearly 50% of businesses worldwide already use these systems.
Specific examples of AI at work include predictive analytics that can accurately predict upcoming trends, business hazards, and opportunities. By employing scenario simulations and robust data scrutiny, generative AI can aid in optimizing supply chains. For instance, AI can pinpoint operational inefficiencies and propose remedies for optimizing procedures. AI can also take over administrative and repetitive duties, giving employees more time for high-impact activities and long-term planning. This can lead to higher productivity and a more nimble and strategic headcount.
Generative AI has also revolutionized customer interactions. Instead of old-school chatbots, you can use AI to create a highly engaging customer service experience. Moreover, generative AI can assume the role of a co-pilot to your employees, accelerating the tempo of research and development, hastening the phases of ideation, prototyping, and experimentation. This can speed up product development cycles.
Key business functions – operations, sales, marketing and software engineering - will account for 75% of the expected growth in use of AI. This massive impact on business fundamentals underscores the imperative for companies to embrace this technological wave and proactively prepare their workforce for the impending transformation. AI will change your business, for better or for worse. This is where Darwoft can help.
HOW AI WORKS
To gain a competitive edge using AI, it may be useful to first understand the history and the foundational layers of these technologies.
Generative AI uses a computer instruction set to craft seemingly original content like text, images, or audio which can be remarkably human-like in execution and form. They learn from an immense pool of examples extracted from the vast expanse of the internet. To create a human-like experience, generative AI “ingested” human works and now can create a simulation of the kinds of works it was trained on.
Programs like ChatGPT and other large language models generate lifelike responses by statistically analyzing text fragments that naturally succeed one another. Pictures drawn with AI can look as if they took an artist months to create: writing created with AI can be nearly as exquisitely phrased as any novelist’s work. These capacities are statistical outcomes of a system being trained on a vast array of already-existing human data, such as Wikipedia, blog posts and books written by human beings.
CAUTION! HALLUCINATIONS AHEAD
Generative AI doesn’t actually “understand” the information it is providing to you, and the systems can thus create inaccurate “hallucinations”. For example, if you train an AI on legal documents, it will be “good” at creating new legalistic-sounding documents. However, this does not mean the AI system is creating accurate documents. Take a look at this court case for a reminder about the perils of relying on AI! Always keep a human in the loop, and carefully review all business-critical content you are publishing.
Training is key. AI systems can be trained on specific types of data and “tuned” on various inputs to become better at creating one type of content. Once a large language model is trained on a specific data type, then it can readily create new works that are similar to that original data. For example, if you train an AI on every positive customer service interaction in your system, the system should then be able to monitor and create new customer service interactions that match to the best customer call interactions. AI can also be trained on a variety of other types of data to create better customer experiences, content like blog posts and even visual and audio content.
HISTORY OF AI
The systems we call “AI” today came out of decades-long analytical projects by innumerable computer scientists and neurolinguistic researchers. Generative AI traces its origins back to major research universities in the 1950s and 1960s, when the first ideas of the Internet were gestated. Initial efforts around “smarter computer systems” centered on rule-based systems that could possibly emulate human thought patterns. Mathematical-based systems such as Variational Autoencoders (VAEs) and Recurrent Neural Networks (RNNs) eventually began to demonstrate an ability to generate novel content. Over time, generative models gained prominence in speech recognition, image processing, and NLP.
By the turn of the millennium, probabilistic graphical models called Bayesian networks were used for new advances in robotics and computer vision. Researchers also began to deploy statistical models to generate new sequences of data based on human input, called Markov models. In 2014, these developments came together in the work of Ian Goodfellow and colleagues, who proposed generative adversarial networks (GANs) to kick-start the generation of seemingly authentic new content. Then in 2017, a seminal public paper published by Google researchers described a novel architecture for large language models, which blew the lid off AI research. The transformer model accelerated everything, and soon generative AI “large language models” were released by several companies. These systems are now used by millions of people.
Recent developments include the following:
- GPT-2 and GPT-3 (2019, 2020): OpenAI's generative pre-trained transformer (GPT) models marked a significant leap in the field of GenAI for text. These systems demonstrated the ability to generate coherent and contextually relevant content.
- DALL-E (2022): OpenAI launched DALL-E to the public. DALL-E is a deep learning model that can generate digital images from natural language prompts.
- ChatGPT 3.5 (2022): Open AI released ChatGPT, a conversational chatbot based on GPT, and the platform reached one million users within five days.
- Midjourney (2022): Launch of a new image generation LLM that can create original content based on written prompts.
- Chat GPT 4 (2023): Open AI released ChatGPT 4, which built on the foundation of 3.5 with massive improvements, and use of 1 trillion parameters.
- Claude 2 (July 2023): Anthropic, the AI startup co-founded by ex-OpenAI execs, released a new text-generating AI model, Claude 2, which is designed for ethical AI use.
- Meta LLaMA (August 2023): Facebook parent company Meta released an open-source transformer-based auto-regressive language model, free to developers.
- SeamlessM4T (Sept 2023): Meta introduced the Massively Multilingual & Multimodal Machine Translation for speech-to-speech, speech-to-text, text-to-speech, text-to-text translation, and automatic speech recognition for up to 100 languages.
HOW DARWOFT CAN HELP
Darwoft has expertise in the exact technologies needed to implement generative AI in the precise ways that can accelerate your time to market and transform your customer experiences. We can make AI happen for your business.
AI is still a new technology to most companies. However, Darwoft’s principals have been instrumental in this field for over a decade, delivering key AI innovations for global companies such as Intel and Microsoft, both of whom have been key innovators in the AI tidal wave. It is critical to have experienced personnel working alongside your team at the outset of any AI project. We know what we’re doing.
The business-critical capabilities we bring to your AI project are the same ones that Darwoft brings to every professional engagement. It is easy to say that a team has good communication – but we prove it every day, with every project. We have excellent inter-term communication and teamwork, and we work in a highly collaborative manner. For a large-scale AI project, we have historically undertaken the tasks listed below, but we do our work in a flexible manner designed to take into account your business needs.
AI is powered by a big data collection and training process. In AI the maxim “garbage in = garbage out” is more true than ever. We make sure your system is not built on garbage, but instead has a firm foundation. To start the process, our data engineers and data specialists first work closely with a cross-functional team who understand your business. We focus on the process of optimizing your data from raw structured or unstructured data into an AI-ready format. Our data scientists then clean and prepare data for analysis. Our trained data analysts then analyze the data prior to ingestion so that we can understand how the AI will make sense of your dat and how to optimize outputs for your specific business purpose.
After these initial steps of data readiness and ingestion, our machine learning engineers train the machine learning (ML) models with your data. Our software engineers then deploy the ML models to build AI software. Our agile processes ensure that risks can be managed all the way through, and that we can easily roll back changes if an ML deployment is found to not be effective for your business needs. Our team follows a framework called “MLOps”, an extension of DevOps, a standard process that improves software quality with continuous feedback between operations and development.
Working with advanced generative AI is a non-trivial task for any engineering team. Our teams are experienced at working with complex systems, and are partnered closely with our data scientists and our machine learning experts. Our software engineers take the optimized data and deployed ML models and deliver components designed for long-ter sustainability. Our teams can build and sustain both a solid front-end experience as well as a scalable backend infrastructure, that can support your customers around the world, so that your business can grow on a firm foundation.
All the AI outputs in the world are useless unless you have a good user experience for both your internal users and your external customers. That’s why Darwoft ensures that every AI system we design includes the oversight of a trained User Experience expert, coupled with a set of UI/UX designers. Our teams work against defined business requirements to present AI-created data in a way that is optimized to your specific business purposes. We assign domain-specific UX experts to your project – we can deliver finance-experienced or healthcare-experienced UX personnel to your systems, so that your business benefits from their decades of experience and expertise.
All the software we deploy is extensively tested. This is also true of ML models and AI systems. ML models are continuously updating themselves with new data without any human intervention. Therefore, it is essential to continuously test the system as well to ensure that there has been no degradation of data, outputs or business value. We test to also remove implicit and explicit biases and hallucinatory material from the system (which are common risks of any AI system).
The advent of generative AI is seen by many tech observers as a massive sea-change in the way computing systems will interact with human beings in the future. Because these systems can “understand” standard human expressions and language, it is possible to use AI to ask a computer system to do complex tasks, yet you no longer need to be a programmer or computer scientist to input those requests in a set of arcane instructions called computer code. You can move faster and smarter with AI, but only if you build the system in a way that is optimized for your actual business needs.
If your company chooses to embrace generative AI as a key technology, you may need to make a significant investment in order to maintain data quality, and to train your system’s engineering team to work effectively with AI technologies. However, we believe this kind of investment can pay off for any business who is willing to make the commitment.
We take generative AI seriously, and we believe this technology has the potential to transform how we interact with computing systems in the coming years. However, we also know that there too much hype and over-selling in this industry around AI. For that reason, we try to ground our work in real-world deliverables. We don’t work on speculative projects – Darwoft delivers real business value.