Artificial Intelligence is transforming the business landscape in a big way. To underscore its immense potential, consider the projected market expansion: from a value of $242 billion in 2023, it is expected to surge to a $739 billion by 2030.
One of the most crucial applications of AI lies in its integration into user interfaces. AI Interfaces are born from the synergy of generative AI and conversational AI, ultimately forming AI Platforms. These AI platforms have the potential to grow to hundreds of billions in market size by 2030.
If your organization is keen to capitalize on the AI revolution, then you need to start thinking about incorporating AI interfaces into your IT strategy. Let’s break it down.
What are AI Interfaces?
AI interfaces are software interfaces that incorporate Artificial Intelligence to facilitate human-computer interaction (HCI).
More commonly known as Intelligent User Interfaces (IUIs), they are designed to understand human input, predict user intent, and automatically produce contextualized results based on what the user needs. These results may either be, for instance, to answer a query about a product or navigate through a website without a need for human intervention.
Traditional UIs typically have strict interaction patterns and require users to understand the specific navigational elements of the software. This presents a challenge, especially for specially-abled users who may not be able to interact with the software through the limited UI.
AI interfaces simplify and personalize UI. This eases the navigation and improves the overall user experience.
Here are some key capabilities of AI interfaces:
- Natural Language Processing (NLP): AI interfaces often incorporate NLP capabilities to understand and process human language. Virtual assistants like Siri, Google Assistant, and Alexa are prime examples of AI interfaces with NLP capabilities.
- Personalization: The ability to analyze user data and behavior to provide personalized outputs such as recommendations, content, and user experiences.
- Predictive Input: The ability to predict user intentions based on historical data and user interactions. Autocomplete suggestions in search engines and email clients are common examples of predictive input in AI interfaces.
- Computer Vision: AI interfaces can employ computer vision technology to recognize and interpret visual data from cameras or images. This enables applications such as facial recognition for tasks such as user authentication.
- Sentiment Analysis: The ability to analyze text or voice input to determine the sentiment or emotional tone of the user. This can be used in customer service chatbots to gauge user satisfaction and adjust responses accordingly.
- Automation: AI interfaces can automate repetitive tasks and decision-making processes.
What technology powers AI Interfaces?
The primary AI technology used to create AI interfaces is Generative AI. In fact, it is through Generative AI that other AI implementations take effect. So, what is generative AI?
Generative AI is simply a type of artificial intelligence that is capable of creating new content. This content can either be text, images, audio, video, or other forms of media through which humans naturally communicate. Generative AI can also create synthetic data, which is artificial data provided by algorithms after real-world data is processed.
How is generative AI able to produce all this? It does this by learning from existing data and generating similar, original outputs.
Machine Learning (ML) is the key component here, and generative AI is made possible through various diffusion models — algorithms through which textual, auditory, or visual data is transformed into synthetic textual, auditory, or visual results.
Diffusion models work with deep neural networks and differ based on the type of data they are trained to work with. The diffusion model used by an AI interface depends on the form of input and output expected from users.
For instance, text-based AI platforms like ChatGPT make use of Large Language Models (LLMs). Related to Natural Language Processing (NLP), these models use an algorithm that understands natural textual structures. It then processes textual requests and generates contextual human-like responses.
Speech recognition software uses more advanced NLP models like the Hidden Markov Models (HMM) to understand and create sound waves. Image processing and generation programs make use of denoising diffusion probabilistic models, for instance, to understand image abstraction and imitate real-life images using synthetic data.
Additionally, prescriptive ML models can be used to create synthetic data to train ML algorithms further or aid with platform automation.
Use cases for AI Interfaces
AI interfaces have found uses across almost all industries. It’s difficult to imagine an industry that is not adopting this development.
Here are some examples, from healthcare to finance and more.
AI-powered platforms can be used in healthcare to achieve the following outcomes:
- Diagnosing health conditions
- Predicting future health risks
- Understanding treatment variability
- Personalizing care processes.
For instance, specialized generative adversarial networks (GANs) or other image-processing diffusion models can be used to understand X-ray scans and provide diagnostic reports on the condition of the bone.
For drug discovery and development, synthetic data can be used to train ML algorithms on drug structures and compatible drug compounds to generate tailored medication. Drug progression and patient health information can be analyzed to predict the best prescriptions and care processes for a patient.
AI interfaces are also being used to support medical research. They can simulate treatment results without having to engage in human-based medical trials.
Across the finance sector, generative AI can be used to provide insights into multiple facets of financial management. For instance, banks can use AI interfaces to analyze customer credit profiles. They can utilize ML-powered solutions to create behavioral baselines, detect abnormal access behaviors, and respond by automatically locking out threats to prevent fraud.
Talking of behavior, there is an emerging trend known as the Internet of Behaviors. Please check it out and see how your organization could take advantage.
AI interfaces can also be used to facilitate trading in financial markets. For instance, Morgan Stanley uses a GPT-4-powered chatbot to assist its financial advisors, whileDeutsche Bank uses AI to scan the investment portfolio of large clients and notify them of risks based on known financial factors.
3. Retail and E-Commerce
In the retail and E-commerce industry, the use of AI interfaces is even more prominent. We’ve seen AI-powered data platforms used to discover target audiences and generate leads.
A more prominent use is in personalizing marketing campaigns. Here, customer data is ingested into a central dashboard where customer profiles can be created automatically, based on interests and demographics. This data is also used to personalize product recommendations and promotional messages.
Customer conversations over the phone may also be recorded and run through a relevant NLP model. The model then summarizes complaints, contextually understands pain-points, and automates resolution.
Historical data on customer demands can be used to predict the optimal inventory to meet future demand without waste.
Other use cases of AI interfaces in the retail/E-commerce industry include:
- Product quality analysis
- Personalized checkout
- Real-time augmented reality (AR) for product inspection
4. UI/UX design
When it comes to the UI/UX design industry, image-generating diffusion models are the go-to solutions. These models power specialized programs that have the capability to transform text prompts or scanned images into mock-ups and complete prototypes.
For instance, UIzard, which is used in top companies like IBM, Tesla, and Google, is trained to automatically create designs for website and mobile app wireframes, landing pages, SaaS web apps, and iOS mobile apps, among others.
5. Customer servicing
Another customer-facing application of AI interfaces is in the customer servicing industry. Here, automated chatbots make use of NLP models to understand text or voice requests.
The chatbots then provide a contextual response either clarifying the issues faced by customers, providing steps for resolution, or helping users to navigate through the platform.
Case studies of successful AI Interfaces
For a closer insight into how AI interfaces are currently being used, let’s look at case studies of the Hippocratic AI and Bank of America’s AI, Erica.
Hippocratic AI is an ML-powered solution for medical diagnosis. Pioneering a «safety-first» large language model (LLM) for ML processing, the solution has achieved high pass marks in multiple medical examinations.
These examinations include the North American Pharmacist Licensure Examination (NAPLEX), the American Board of Urology Licensing Exam (ABU), and the Certified Professional Compliance Officer (CPCO) exam, to mention a few.
Serving primarily as a customer-facing AI platform, the AI interface is designed to receive prompts from patients and provide contextual responses to customer requests. These requests are run through Hippocratic AI’s LLM, which is trained with evidence-based medical content and Reinforcement Learning with Human Feedback (RLHF) techniques. RLHF techniques use continuous feedback secured from health professionals to train and improve the LLM.
Once inputs are processed through the LLM, patients then receive contextual responses on the interface. These responses could include:
- Dietary advice
- Simplified billing information
- Answers to pre-operation questions
- Negative test results for diagnostic inquiries
Hippocratic AI also automates patient onboarding and helps with medical reminders.
Bank of America’s «Erica»
Erica is Bank of America (BoA)’s AI-powered virtual assistant, incorporated into the bank's mobile app. Launched in 2018, the AI agent facilitates almost 1.5 million interactions per day and has been used by over 32 million BoA clients. With the many uses for the mobile app, Erica goes beyond the NLP models to understand user requests and complaints.
Erica uses algorithms that enable it to continuously ingest text and data. It uses multimodal diffusion models to process different data types and perform multiple actions for the user.
These actions include:
- Studying spending habits and predicting zero balances one week prior
- Notifying customers of credit score changes
- Reminding customers of payments
- Warning about excessive payments
- Managing access to credit and debit cards
Erica is reported to have a 90% efficacy in meeting user expectations.
Potential impact of AI Interfaces in the workplace
Technology is a core part of today’s workplace, and the emergence of AI interfaces will ultimately have some impact
As more businesses adopt these interfaces, these are the key likely outcomes at work.
Humans capable of working with AI will be preferred
Through the use of generative AI, AI interfaces can help with understanding customer requests and quick generation of business insights.
AI interfaces also use synthetic data to automate processes, thus eliminating repetitive tasks. The time saved can be used for more productive business tasks.
In light of this, Salesforce reveals that employees using AI report 90% more productivity and save 3.6 hours a week.
Hence, professionals who possess AI skills will be preferred over those without.
AI could replace humans in some areas
In the more extreme outcomes, we expect that AI will take up certain human jobs. With use cases in every industry and agents installed everywhere, Goldman Sachs predicts that Generative AI could replace 300 million jobs globally.
For instance, the GitHub Copilot may not replace programmers altogether but can eliminate the need for an assistant programmer.
AI Interfaces will still need humans!
The CapGemini Research Institute offers vital insights into the dependency of AI. A majority 90% of organizations have identified at least one instance where AI has caused ethical issues, while 65% have identified discriminatory bias in AI.
Additionally, 60% of organizations have experienced legal complications from the use of AI systems, while 22% have received dissatisfactory remarks from customers due to AI.
These statistics show that, although AI interfaces may be more powerful than humans in certain tasks, there is still a need for human intervention to help with scrutinizing operational processes and decisions.
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