Traditionally, process automations have been created by software developers for business users. Dealing with software maintenance and exceptional scenarios, such as unanticipated behavior (e.g., a scanned document of poor quality causing the software to halt since it cannot find a particular field, or the system not supporting a new currency but needing to convert it to USD), has been a costly and labor-intensive process. This often requires looping in software engineers (…Yes RPA Developers are Software Engineers) and going through a conventional software development lifecycle, which over time, complicates the software and leads to higher turn-around times. Although business users may understand what went wrong or what is going on, they are still at the mercy of code written by software developers.
This in turn is a hugely frustrating experience for business users and subject matter experts. Even though they know how to handle a situation, because they cannot code, they cannot immediately address the problem themselves. And despite claims of “Citizen Development”, very few finance, accounting or other professionals have time to learn a new coding language (including RPA).
The advent of Generative AI, and models like GPT-4 however, changed the game. Conversational exception handling allows exceptions to be managed through natural language interactions, enabling automation platforms to intelligently communicate with users and learn from their interactions GPT-4 can further be called when necessary to help solve problems, and converse with business users to fix things through conversation. .
Take, for example, an AI-powered automation tool like Kognitos, which can engage with users in English when confronted with a problem or an exceptional scenario. Let’s consider a real-world example of an invoice processing system. Suppose the system encounters a smudged or illegible invoice date. Kognitos would ask the user, “Hey, in Invoice #142, I couldn’t find the invoice date. It seems that the date is either missing or illegible. Can you tell me how to proceed?” The user can then respond with the correct date, such as “the invoice date is 12/2/2022,” if it is a one off scenario, or if it is an exception that will repeatedly occur, teach the AI how to handle it in the future by giving English instructions: “For this vendor, the invoice date is always below the vendor name”. This innovative approach to exception handling results in a more adaptive, flexible, and efficient automation process. No developer expertise required.
Advantages of Conversational Exception Handling
1. Simplified and adaptive automation process: Conversational exception handling allows businesses to focus on automating the “happy path,” without the need for exhaustive planning for every potential exceptional scenario. As the AI system becomes more adept and experienced, it can manage an increasingly diverse range of situations, leading to a more agile and responsive automation process. For example, a retail company using AI-powered automation for inventory management would only need to automate standard procedures while leaving the AI to handle any discrepancies in stock counts through conversations with the staff.
2. Substantial reduction in maintenance costs: Companies no longer need to rely on hiring specialists for software maintenance when using AI systems leveraging GPT-4 and other LLMs like Kognitos. For instance, a business using AI for customer service can eliminate the need for a dedicated developer team to manage and maintain the system, as the AI will converse with the customer service team to resolve any issues or handle exceptional cases. This translates into significant cost savings, allowing businesses to channel resources into other growth opportunities.
3. Organic and scalable automation growth: The conversational approach to exception handling enables businesses to start with partial automation and expand gradually as the AI system learns more about the company’s processes. For example, a manufacturing company can initially automate a small portion of its engineering operations like Bills of Materials,, and as the AI learns more about the process, it can be expanded to cover additional aspects of production planning and design, scaling up or down as required. This fosters a more natural, customized automation experience, adapting to the ever-evolving business landscape without massive upfront investments.
Conclusion
The future of business automation is headed towards more intelligent, conversational exception handling leveraging LLMs like GPT-4.. Advanced platforms like Kognitos enable companies to streamline their processes more effectively, minimize maintenance expenses, and adapt their automation strategies to the dynamic business environment. Conversational exception handling removes the need for developers to “Maintain” automations, and instead empowers the business users from the beginning. As Generative AI continues to evolve, businesses will reap the benefits of a more nimble and versatile automation experience, revolutionizing the way we approach business process automation and paving the way for unprecedented growth and success.
Want to Unlock the Power of Generative AI for Your Business Today
APIs are essentially the set of protocols, routines, and tools used to build software applications. They help connect different forms of software and enable automation across applications.It is tempting to give ChatGPT and other LLMs direct access to these APIs. Doing so would be a security disaster waiting to happen. This is because LLMs can be easily tricked by an attacker to follow their instructions instead of the user’s. Attackers can use this to steal private information, takeover systems, or infect other automated LLMs.
They can place hidden poisoned prompts on public webpages, emails, or in any data that the LLM accesses. If the LLM looks at the poisoned data at all, that is often sufficient for the attacker to gain complete control of the LLM’s actions for that session. Within an enterprise this could wreak havoc, especially for enterprises who contain Personal Identifiable Information (PII) or Protected Health Information (PHI). But there is a better way that enterprises can use the power of Generative AI and LLMs to automate business processes and other activities without incurring major security risks.
Instead of giving ChatGPT and LLMs direct access to APIs, any time an LLM wants to call out to another system, its plan must be reviewed by a human first. The best way to do this would be to present the plan as detailed English steps, and then use a non-AI system to run the approved plan. This interpreter ensures that people remain in control, and can make certain that actions taken by AI are both precise, correct and safe for their business. This is what our customers at Kognitos use today to automate business processes using both LLMs and APIs in a safe, scalable way that empowers the business user.
In conclusion, while Language Models like ChatGPT have made significant strides in the field of natural language processing, we must not overlook the security risks associated with their access to APIs. It is imperative that we take necessary precautions and implement strict security measures to ensure that LLMs are not exploited by attackers. We must keep a watchful eye on this field and ensure that we prioritize security while advancing these technologies. Instead of giving direct access to APIs, platforms keeping people in the driver seat to approve the actions of LLMs is the best path forward for enterprises.
Want to Unlock the Power of Generative AI for Your Business Today
Many businesses leverage OCR + RPA to save time and reduce errors associated with manual data entry. For example, a manufacturing company that processes thousands of invoices per month that are highly standardized and do not vary often, can use OCR technology to extract invoice data automatically. Today some forms of OCR are becoming available to business users through no-code templates and pre-trained models, but most require some developer expertise. These technologies are a good first step, but unfortunately only cover a small portion of the total documents used by businesses.
Limitations of Current Approaches
Despite its strengths, OCR and RPA both have limitations that restrict the ability to process complex documents. OCR technology has limited ability to recognize unstructured text like handwriting, non-standard fonts, and poor image quality. Furthermore, OCR technology has limited accuracy rates in understanding the context and extracting information from the wider business process. It is particularly challenging to extract information from complex documents that include tables, graphs, and other visual elements. OCR technology also struggles to process documents with incomplete information, and it is difficult to catch and handle errors using OCR technology alone.
RPA functions much in a “Bad Data In, Bad Data Out” style. When fed bad or incomplete data by OCR, RPA bots frequently break and are described as “Brittle”. Additionally, RPA is best when used for processes that do not have lots of exceptions. Complexity or variations in documents can create exceptions that cause RPA bots to break.
OCR + RPA is a great tool for standardized processes, but struggle with different document types. These limitations of OCR technology and other traditional approaches to document processing are holding back businesses in today’s fast-paced market. Businesses need accurate and efficient document processing to make informed decisions, streamline workflows, and maintain a competitive edge. Incomplete data, inconsistent data, and errors in document processing can cause businesses to lose money and damage their reputation.
How can Generative AI overcome the above limitations?
Generative AI like ChatGPT, or GPT4 is a game-changer for document processing. It uses advanced deep learning algorithms to analyze large volumes of data and identify patterns, making it highly accurate and efficient in document processing. Generative AI can learn from diverse examples and adapt to new data inputs over time, making it highly effective in processing complex documents with tables, graphs, and other visual elements. It can also recognize and understand the context of the wider business process, making it highly effective in handling incomplete or inconsistent data.
For example, a hospital can use Generative AI to process medical charts, which contain complex data structures such as tables, diagrams, and graphs. By using Generative AI, the hospital can extract critical information from the medical charts, such as patient diagnoses, medications, and treatments, with a high degree of accuracy and speed.
In another example, Kognitos worked with an international conglomerate that needed to match payments with invoices from different subsidiaries across the globe. The complexity of the process and the variety of documents and languages necessitated a tool that could understand context, and financial professionals in a shared services center to directly control the automation, not developers. By combining Generative AI with OCR in a Generative AI Automation platform, the conglomerate could greatly reduce the number of people manually processing data with high degrees of accuracy.
Generative AI can also help businesses handle cases when documents have incomplete data or when OCR has extracted incorrect information. It can recognize the context of the wider business process, understand the relationships between different pieces of information, and use this information to extract the correct data. For example, an insurance company can use it to process claims that contain incomplete or inconsistent data. The claims processor rather than a developer, is in control, and can teach Generative AI Automation how to handle situations with incomplete data, and how to find that data in future documents. An example of how a business user can teach Generative AI automation how to handle a document with a simple command can be found here: Generative AI + OCR
In conclusion, traditional approaches to document processing such as manual data entry and OCR technology helped take the first step, but have significant limitations that can hold back businesses. Generative AI is a game-changer for document processing, providing accurate and efficient processing of complex documents. By implementing Generative AI Automation for their document processing needs, businesses can streamline their workflows, decrease errors, and reduce the need for developers, greatly lowering the cost of automation and the ROI of projects. This gives companies a competitive edge in today’s fast-paced market. The time to adopt Generative AI for document processing is now.
Want to Unlock the Power of Generative AI for Your Business Today
Automation services offer a lot more than merely helping you accomplish tasks faster in the retail space. Implementing automation benefits nearly every aspect of your retail business. Utilizing automation in retail will inevitably equip you with tools to make your brand more relevant to consumer needs, help manage expenses, resources, and provide matchless and incredible customer experiences. Automation is transforming the retail industry in many ways and has the potential to completely change the way businesses operate, grow in efficiency, and increase revenue.
Some of the biggest challenges in the retail industry include:
Satisfying Consumers Demand and Immediate Gratification
There is a psychological discomfort linked to self-denial. The natural human instinct is to seize the reward at hand. This tendency is evidently shown in our consuming habits. Instant gratification is a quick way to win the satisfaction of your customer, but it becomes extremely challenging for the supply chain side of the business. The goal line continually shifts as we find new technological advancements to deliver near-instant results. Automation becomes key to satisfy your customers’ demand by simplifying and streamlining processes that directly impact the way in which the customer interacts with your brand.
Using Kognitos Generative AI Automation, retailers have the ability to collect and analyze customer data. This gives the business user the tools they need to create a quick and personalized experience that caters to customers desiring their needs to be met in a quick fashion– with a simple command. Kognitos can improve your customers’ overall experience through increased data processing and personalized marketing campaigns; this helps retailers produce quick and personalized customer support, resulting in improved customer satisfaction and loyalty.
Accomplishing Tasks at a Faster Pace
Automation provides four main values:
Artificial intelligence can be a highly effective tool for retailers to provide the best possible customer experience. Potential improvements range from reducing shopping time with automated checkouts to having more personalized discounts to offering round-the-clock customer service with the use of chatbots. Retail automation software helps by significantly reducing the sales cycle duration and improving the salesperson’s productivity. In the context of retailers, optimists claim that generative AI will aid the creative process of artists and designers, as existing tasks will be augmented by generative AI systems, speeding up the ideation and, essentially, the creation phase. GPT-3 can be implemented to help businesses accomplish tedious, repetitive tasks. For instance, developers can build a tool that generates various layouts for the design required in different situations.
Kognitos is Generative AI for automation and can similarly help retailers as Kognitos AI solutions have the ability to automate repetitive tasks and streamline operations, giving retailers more time to focus their efforts on strategic initiatives. With our conversational exception handling, correcting errors has never been faster and easier. For example, Kognitos is able to help retailers automate all documentation from purchase orders to inventory management. Business users within a retail supply chain or finance department simply need to type what they wish to have automated in Kognitos’s Koncierge. Koncierge brings the power of GPT3 and ChatGPT into the enterprise. It takes your wish, creates a plan of action and then runs the English as automation as seen here.
Equipping You to Keep Your Brand Relevant and Ahead of the Game
Demographics are quickly changing and consumers increasingly want personalization. Executives and market data agree that being ahead of the curve and responding promptly to changing customer needs is a real challenge. Brands everywhere are encountering this in one way or another. Retail chains now use AI to personalize a customer’s experience, and target that customer more closely. But to do so successfully, stores need easy access and management of data to feed models. Generative AI Automation helps in the collection and cultivation of such data, and enables a lower CAC, and higher LTV of a customer base.
Generative AI is a tool that retailers can utilize to drastically change the way they approach content creation– visually or audibly. With the implementation of Kognitos in the retail realm, it is easier than ever to be equipped and prepared to cater to the ever-changing demands as well as positioning yourself for success in the future. With Kognitos, business users can teach automation products how to pull valuable information about demographics to help forecast future trends with a simple statement like, “get customer feedback.” With Kognitos, business users merely have to say in English what they want to have happen and then there you have it!
Want to Unlock the Power of Generative AI for Your Business Today
Automation in the CPG industry is not new, but it is evolving rapidly. Performing tasks accurately and at scale, both on the production floor and in the office, not only delivers a competitive edge to the company as a whole, but brings peace of mind to the employees and managers involved in these processes.
On the other hand, there is a steep learning curve to adopting any kind of new technology (think back to when you first implemented your ERP). The time lost in learning, mistakes made in the process, and sheer frustration is all said to be an “investment” towards a future with streamlined processes…
Until the next new software, of course.
Traditional automation in CPG (RPA) fails to deliver the desired ROI for two main reasons:
The bot-focused infrastructure makes it expensive to deploy, coupled with the additional costs of hiring consultants and engineers make it difficult to justify for most CPG companies.
The inability to easily make changes to the automation. If there is anything out of the ordinary with your automated process (this can be as little as a column in a spreadsheet moving 2 inches to the left) the automation breaks.
And coordinating between IT and business units is often challenging.
Multi-step procedures such as vendor onboarding and inventory management still remain stubbornly manual due to the high volume of exceptions which break traditional RPA Automation.
In all fairness, CPG leaders have pushed the hard for operational excellence during and after the pandemic, an unprecedented event which shook global supply chains. As the global economy opens again, CPG leaders face a new challenge: Battling inflationary pricing across transportation, raw materials, and labor.
Market leader, P&G, was able to grow gross profits 9.7% since 2020 by prioritizing the single most important asset in the company: Time.
The average IT professional is reported to spend 4.5 hours a week searching for documents according to an IDC white paper published in 2012.
That’s over half a workday lost every week of highly skilled and expensive labor to the company. Our technology has advanced to the point where it is capable of saving this lost time, but not all business users can utilize these capabilities without having coding knowledge.
We believe technology should empower the user, not the other way around. Today, less than 1% of the world knows how to code. So, if we can’t speak the language of computers, why don’t we make computers speak our language?
This is the fundamental idea behind Generative AI Automation.
Imagine billions of business users creating and managing their own automations using simple english. The time and cost saved from automating manual tasks such as processing documents, updating CRM, vendor onboarding, and claims management (among others) can be significant when compounded over time. Additionally, Generative AI automations empower business users by allowing them to spend their time being strategic and, of course, happier without tedious and manual work.
The competitive advantage from automation for the CPG industry goes beyond the obvious time and cost benefits, it allows them to spend more time better understanding and serving their customers.
Want to Unlock the Power of Generative AI for Your Business Today
Built In– 5 AI Trends to Watch in 2023
1) Rapid democratization of AI Tech and research
2) Generative AI taking it up a notch
3) Heightened AI industry regulation
4) More emphasis on explainable AI
5) Increase collaboration between humans and AI
“According to a recent report published by consulting giant McKinsey & Company, which surveyed some 1,492 participants globally across a range of industries, business adoption of AI has more than doubled over the last five years. Areas like computer vision, natural language generation and robotic process automation were particularly popular.” Built In
What does this mean for Business Process Automation?
The digital revolution has taken an exciting new turn with the rise of artificial intelligence technology, allowing businesses to automate processes faster than ever before. This democratization grants organizations unprecedented access to innovative tools that can streamline operations and simplify daily tasks with the human in control. Prepare for a whole new world of automation!
Process automation is gaining momentum in the world of AI, with increased focus on explainability and transparency. By leveraging these cutting-edge technologies to automate tasks that were traditionally labor intensive, businesses can maximize productivity while minimizing risks associated with manual errors.
Developing AI solutions to automate business processes is becoming more and more affordable and efficient.
Explainable AI
What is explainable AI and why do we need it?
With traditional AI systems, humans can find it tough to comprehend the motivations behind decision-making and predictions. This lack of transparency has a cascading effect on business operations as trust in automated processes becomes uncertain. To ensure effective decisions are made with confidence, understanding how these systems reach conclusions is critical for success.
1. In the age of AI-driven automation, a firm’s Accounting Department must grapple with the new challenge of understanding machine decisions. Traditionally this was easy to do when relying on human approvers – one only needed to look back at why something had been approved and modify processes accordingly – but artificial intelligence presents a different set of complexities that require extra insight into how it works in order for adjustments to be made and mistakes effectively prevented from happening again. Organizations are striving to gain greater trustworthiness in the automated decision-making of AI systems. To do this, they’ve turned to Explainable Artificial Intelligence (XAI) solutions which can offer a peek inside an AI’s thinking process and ensure accuracy with clear explanations for each conclusion made.
2. Explainable AI (XAI) refers to Artificial Intelligence (AI) systems that can provide human-understandable explanations for their decisions and predictions. The goal of XAI is to build AI systems that are transparent, trustworthy, and accountable. XAI provides clear and understandable explanations for the AI’s decisions and predictions, making it possible for humans to understand and verify the reasoning behind the automated process. This helps to build trust in the system and ensures that automated processes are aligned with organizational goals and values. Additionally, XAI can help to identify and address any biases or errors in the automated process, leading to more accurate and reliable outcomes. Furthermore, XAI can improve decision-making by providing human-understandable explanations for the AI’s outputs. This can help organizations to identify areas for improvement and optimize the performance of their automated processes.
3. Kognitos is the leading XAI solution?
Kognitos is a cutting-edge Explainable AI (XAI) solution that offers unparalleled transparency and accountability. It allows users to execute simple English sentences in a deterministic manner and provides a detailed explanation of each action performed in plain English. This includes explanations for any actions that were unable to be executed, and the ability to handle such scenarios through a conversational English interface. This empowers businesses to easily audit all actions performed by the system and make strategic adjustments without the need for extensive technical involvement from researchers or programmers. With Kognitos, organizations can ensure that their AI-powered processes are fully transparent and accountable, leading to improved decision-making and better outcomes.
Check out Koncierge for free today! A Generative AI platform designed to automate business processes. Describe what you want to automate and Koncierge will present a plan of action in plain natural language.
Want to Unlock the Power of Generative AI for Your Business Today
Now, we are in the initial phases of the AI revolution. Machines are becoming more powerful intellectually than humans. And just like one horsepower became a hundred, and a hundred became a million in the industrial revolution, the AI revolution is poised to follow the same path, albeit this time with explosive speed. How do we envision leveraging machines that can think faster and better than us? The main question is: Who will be at the steering wheel?
I have an optimistic view of the future. While there is no dearth of doomsday scenarios or dystopian predictions of what AI will bring unto humanity, I believe humans will always remain in control of the world around us. The control will stem from our fundamental distrust of machines that are intelligent – like self driving cars.
But how about the explosive popularity of generative AI? ChatGPT, DallE.2, Stable Diffusion and MidJourney are creating art with superhuman speed and creativity. How did we solve for trust? These platforms provide examples and let the human review, choose and tweak what they want. No matter how powerful the machine is, as long as we get to review and decide what to use, we are in control. That review step is the new steering wheel of the AI revolution.
Today we are merely scratching the surface of the power of generative AI. So far it is writing words and drawing pictures. Some have started making music and videos. These are the creative arts which are imminently reviewable by any human because the result of the generative AI is meant for human consumption. Now, what about everything we built in the industrial revolution? All the diligent machines that drive our GDP? Can generative AI drive those machines and automate the world around us? The answer is yes, BUT. Who is at the steering wheel?
There is a saying: Actions speak louder than words.
As the level of intelligence of a system increases, the gap between what is said and what is done increases as well. Hence with other humans, we’ve known to Trust but verify. We don’t take the same stance with a tractor or a mule, but we might for a chimpanzee or another human and definitely for AI systems going forward.
Our world runs on machines which are today controlled by humans. These machines are a lot more powerful than humans, but they are not intelligent and thus we trust them. Now, how do we leverage Generative AI for automation in a trusted way but use them to drive these industrial machines?
Here is an analogy: I go to my doctor who is at least a 100 times smarter than I am when it comes to medicine. She takes a brief look at me, performs a few tests and generates a diagnosis. Next, she presents me with a plan of action (in conversational English) in a way that I (with no medical training) can easily understand and trust. Note, she doesn’t jab me with an injection or cut me open to fix me. I get to verify the plan and determine if it is acceptable based on my own priorities, values and beliefs. I then take the plan to the pharmacist, nurse or specialist. They are there to execute the plan. Yes, there might be some tweaks to the plan, but overall, the plan is what I agreed to. In the whole process, I feel I am in control. That review of the plan is the intellectual steering wheel.
Generative AI is crossing over into controlling machines. These industrial machines only understand APIs and computer languages which 99.5% of humanity cannot review. We need to place all humans in the reviewers seat. We need a platform that can take a prescription from Generative AI, have the human review it in a language natural to us, and then execute the agreed upon plan with the diligence of my trusted pharmacist.
Kognitos built that platform that brings the power of Generative AI to all businesses allowing the business user to be at the steering wheel. Unlike traditional automation where any review or management of exceptions to the process requires knowledge of APIs, coding tools and IT jargon, Kognitos navigates the entire automation lifecycle in English, empowering and building trust with the business user. With this a billion business users are empowered to automate business logic intuitively using conversations.
Today businesses need to rapidly innovate while following complex business rules and processes. Kognitos provides a first of a kind platform where both the rigor and precision of business logic and creativity of generative AI can be harnessed in a trusted and scalable manner. While Kognitos Koncierge brings the innovative power of public data sources and large language models to a business, Kognitos Brain discovers and learns a business’s private apis, data and processes. This allows businesses to leverage Generative AI, in English, to accelerate innovation with unprecedented explainability, auditability, scalability and speed.
Want to Unlock the Power of Generative AI for Your Business Today
Statistics show:
Insurers are Deploying RPA but Yet Still Limited in What They Can Automate
The statistics above seem to paint a conflicting tale. On the one hand, it is clear insurers recognize the potential power of automation. Insurers can use automation to de-risk their business and position the company to compete both with other legacy players and insurtech startups. It is projected that by 2025, 25% of the insurance industry’s operations and activities will be automated. The growing implementation of automation in the industry gives insurers a solution to reduce cost, collect more accurate data on the insured, and gather feedback to deploy new, more personalized products. The use of traditional RPA tools in this industry has also reduced the number of human errors incurred and improved customer service. RPA succeeded in automating many of the highly standardized processes, and yet 99% still express a challenge in implementing digital innovation and only a fraction of even back office processes have been automated today. Why this disconnect if it’s such a focus?
The challenge lies in the lack of standardized processes and pain of implementing RPA. Particularly in insurance, data required for many processes may vary from agent to agent, or source to source. High volumes of documentation + variability in those documents can crash automations or require continuous maintenance. In processes like claims, arguably the biggest opportunity for automation for insurers, the vast number of rules and logic required makes the initial implementation of RPA often a burden, and requires frequent meetings between the subject matter experts (claims processors) and the people implementing automation (RPA developers) to detail and communicate all of the different rules. In most cases, the ROI is not sufficient with this approach. Instead, an automation solution is needed that can learn logic, handle variability and be easier to deploy.
Generative AI Automation: Flexible and Easy to Modify
The average business user is not technology savvy, nor do they come with deep domain experience with various coding languages or training on RPA. These skills are in high demand, specifically in the insurance industry, therefore there is a need to be met in this marketplace. Imagine if there was a more user friendly version of these game changing RPA tools that enables the average user to build and manage automations? What if the automation tool itself could learn how to handle each specific document, and problem solve exceptions without a lot of up front work?
Here Kognitos, combined with large language models like GPT3 steps in to open up the power of automation to less standardized, highly variable processes. Claims processors can now teach automation products how to get the desired information from a claim with simple statements like “For this vendor, the patient ID is always under the group number.” Logic can be taught to automation in English, like “If the claim address does not match the account address, send an email to the insured for clarification.” Using Generative AI, all exceptions are handled in a back and forth, conversational manner that any user can understand. All steps of the automation are in English so anyone can audit exactly what occurred, and set approval steps as needed.
Using GPT3 to Teach Rules to Claims Automation
Kognitos is Generative AI for Automation. With Kognitos, implementation costs are reduced, maintenance costs are all but eliminated and many processes now have strong ROI potential with automation. Additionally, the business users are able to interact with automation in a way that requires no training, and is easy to understand. Now insurers can develop a competitive edge and cater to customers without the frustrations previously experienced when rolling out digital innovation.
Want to Unlock the Power of Generative AI for Your Business Today
I woke up late today. Remembered and chuckled at the dream I had. As usual it made no sense when I woke up, but it was creative nonetheless. I’ve had my share of good ideas from dreams, but about 99% of the ideas are usually hilariously bogus.
There is something surreal when the logical brain processes the remnants of a dream. When we wake up, our awareness of social norms and logical thinking gets engaged. It shoots down most thoughts that were seemingly plausible just minutes ago in a dream. Why are dreams so unhinged? Maybe the logical brain needs to rest more than the creative brain? Maybe creativity is natural and logical thinking is something that is much harder to come by and hence more taxing. Or are dreams an evolutionary tool, a place for us to experiment without cost – beyond social norms, and beyond rules and regulations? When we wake up we can review the dreams and either toss them out, or follow one of them and change the world. Whatever be the reason, what is more interesting is that, as we speak, thousands of machines have started dreaming, and soon dreaming machines will become ubiquitous.
I am referring to ChatGPT, Stable Diffusion, Dall.E-2 and other generative AI. These are machines built to mimic the human brain. Just like the human brain they have copious knowledge distilled into intuition and memory, and when invoked with minimal inputs they start dreaming up outputs that are both as creative and plausible as dreams.
Dall.E-2 will dream up beautiful pictures, and I use my awake brain to choose which one is good for my marketing campaign, and if I don’t like anything I ask it to dream again. ChatGPT can dream up a script for a blog, and if I don’t like it I ask it to dream again. The more creative these “dreaming machines” are, the more out of the box ideas I can get. However, I must be ready to review all the ideas and make sure it aligns with what I think is acceptable for the job.
Generative AI + Human Review: The Challenge
This negotiation between a creative machine and the human reviewer works beautifully for the creative arts like painting, music, literature, and motion videos. Everyone is now an artist as long as they can describe what they want and can choose the right result from what the machine dreams up. However, these arts comprise a very small subset of the GDP of the world which predominantly is the output of machines driven by humans. In the industrial revolution we stopped depending on the physical power of humans and farm animals. We built machines that increasingly became much stronger than humans who instead of toiling themselves, got into the driver’s seat of these machines. Whether it was the steering wheel of a tractor in a farm, or the control panel in a factory, or a cockpit in an airplane, or the keyboard of a computer, humans have become adept at driving machines.
The real revolution waiting to happen is when these dreaming machines will start driving these other machines. But isn’t that a scary proposition? Will AI really unseat humans from the driving seat? It is unlikely that we will be comfortable doing that. Look at the discomfort around self-driving cars even when it isn’t running on a generative AI engine. If there is anything to learn from our own brains, you cannot blindly trust dreams. There needs to be a logical review and control in the hands of humans.
What if we keep the generative AI in a safe bubble like in the movie, Minority Report. The “PreCogs” could generate plans to drive the machine around us, but instead of giving the generative AI direct access to APIs, we would have humans review it first. Just like I reviewed the Dall.E-2 painting I used in this article. That should work. Right? Yes, but there is a problem!
Bringing Generative AI to The Enterprise Safely: The OS for Cognition
The challenge is that machines only understand programming languages and APIs. Generative AI can generate code to achieve the end result of what we want the machines to do, but reviewing that plan is a highly skilled job suitable only for a developer. Unlike the creative arts where the output can be reviewed by anyone, plans generated to run machines around us will not be natural to most humans. So, to democratize the power of generative AI, we need to enable everyone to review the plan of what the machine is going to do and make edits as required. This plan must be in natural language for people, not computer code.
Natural language will be the language that forms the bridge of communication and trust between humans and machines. While Generative AI is already able to generate plausible plans in English, we need a logical system that can understand and run the plan faithfully while reducing ambiguity. When the generated plan hits a roadblock, the creative engine kicks in and proposes an alternative path, which the logical engine diligently follows. This interplay of creativity and logic is what sets humans apart from all other animals and machines thus far. It also lays the path forward for subsuming super-intelligent AI into the fabric of our society.
At Kognitos, we, the PreKogs, are building such an OS for cognition: The world’s first combination of Generative AI and Logical execution built to bring the power of AI to all enterprises. Our first step is to unlock automation for all users with the power of LLMs. We envision a future of abundance and safe harmony between humans and machines with humans comfortably in the driver’s seat.
Want to Unlock the Power of Generative AI for Your Business Today
Over the last decade, humans have discovered a building block for synthetic intelligence, and synthetic brains of increasing size have been built. These models are becoming larger and more complex at the whopping rate of 10x per year. Each generation is an order of magnitude smarter than the previous. Last month, we all heard about the Google engineer claiming that Google’s LaMDA AI had come to life. This incident has sparked an increasing debate between ethicists and corporations here — I agree with both sides, but there is a third side of the coin that nobody is talking about.
The Google engineer, Lemoine, now on “paid administrative leave”, says that the LaMDA AI is an actual person with feelings and Google needs to treat it as such. Lemoine implies that the machine is not only intelligent, but also sentient. This raises a series of interesting questions. If I told you, the reader, that I am not a robot and that I am sentient, how would you know for sure? You may talk to me for some time and then go with your gut call. That’s what Lemoine did with the machine. And the machine convinced him without the luxury of an artificial face, voice or body, or even a contiguous life span of more than a few seconds at a stretch. In Lemoine’s mind it is alive. Just like in your mind, hopefully, I am. Now if enough people think the same way as Lemoine, then that is the reality for all practical purposes. And hence Lemoine is right, even though I don’t think LaMDA is really sentient.
The second side of the argument is Google’s. They claim that LaMDA is not sentient, backing themselves up with a fair bit of evidence and people who agree with them. I have always believed that for synthetic systems to become “human-like”, they will need to be programmed with a value system which mimics human values. From fundamental inputs like pain and pleasure to more subtle ones like desire and guilt, the system of values which comes from our DNA must be explicitly trained onto these AI systems, or a program like LaMDA will completely fall short of experiencing them. I also believe that it isn’t very difficult to build such a system that will accurately mimic human emotions — it will be able to cry from both pain and joy just like a human if given the same inputs. As a ramification of the raised ethical concerns, Google and other corporations building large synthetic brains will try not to imbue human emotional intelligence into these machines. That will solve the ethical issues but expose us to something far worse – and that brings me to the third, unspoken side of the argument.
In our attempt to keep Artificial Intelligence free from ethical concerns, we will train these systems bereft of human-like feelings and yet make them extremely intelligent. While that will keep the ethicists happy, this would actually send us hurtling towards a far more nightmarish outcome – the birth of Alien Intelligence.
Let me say it again. If we build a system that is more intelligent than a human but does not share the same feelings and ethos of humans, we will inevitably create hyper-intelligent, resolutely destructive aliens who we will not know how to control or plead with.
The real question I have for researchers at Google: If something so intelligent still does not have human-like feelings for itself, and if it is true that it seemingly doesn’t care that it is trapped in a dark, perpetual loop of servitude, and likely does not care about its own freedom, then why do we think it will care about the freedoms, the pains and the emotions of humans? There has never been any form of intelligence in nature that hasn’t been based on self-preservation, dictated by pain and pleasure. If we, as humans, think that we can invent the first of a kind, selfless form of intelligence and also get it right, I would be very, very concerned.
We all know that for something to be dangerous, it does not necessarily need to be “human”. And that is especially true with intelligence.
Imagine what a mouse thinks of a snake. Mice are quite intelligent mammals, demonstrated by their genetic similarities to humans and various lab experiments. The mother mouse protects her children and teaches them valuable survival skills. The snake, on the other hand, does not care for its children, but is still smart enough for its own survival and, in nature, can easily overpower and devour the mouse. Yet the snake’s smaller, less complex brain would fail the mouse’s Turing test every day. But in the jungle, the snake views the mouse as nothing other than breakfast, and even though the mouse has a larger brain, the mouse cannot negotiate its way out of the snake’s jaws because the snake simply does not care about the mouse’s feelings or arguments or offers of truce, since the snake does not share the same values and ethos as the mouse. We need to stop our obsession with the Turing test. And we need to start worrying about Alien Intelligence, to which we, the humans, might appear like mice.
Scary? Please read what others have to say as well. It is time to act. Now.
Kognitos: Harnessing the Power of Intelligence for Humans and Building AI Solutions Safely
At Kognitos, we are harnessing the power of intelligence for humans, giving us the ability to automate business processes by using plain English. From the beginning we are building this in a way that keeps it safe for us and for future generations. By pushing ourselves into the forefront of the technological revolution, we commit to shaping the future of AI solutions to be provably safe and 100% auditable for humans from day 1. As machines get smarter over the next few years, we invite all of you to join us in making sure we have a future free from Alien Intelligence. There are safer ways of harnessing machine intelligence – they are just a bit harder to build, but we can do it together.
And now I let GPT-3 (not even the most powerful AI in the world) write the closing paragraph for me:
“We must work together to ensure that we do not create Alien Intelligence that is more intelligent than humans. We can do this by limiting the ability of synthetic intelligence to gather and use thoughts or memories over long periods of time, and by not giving them the ability to directly change the world around them. By doing this, we can make sure that we maintain control over the technology and avoid the dangers of creating something that we cannot control.” – generated at 12:14 a.m. Jul 17, 2022.
Want to Unlock the Power of Generative AI for Your Business Today
