With so much attention focused on Artificial Intelligence (AI), it’s worth remembering that one size does not fit all. There are specific business-related pain points in mind when a company decides to deploy AI technology, so making the right choices can be a tricky task.
For example, several months ago, an AI related breakthrough was announced – a robot learned and demonstrated the ability to perform a perfect backflip. While it is well acknowledged that the invested research and development for this mission was huge and the commercial potential for some applications is enormous, it is somewhat unclear how this specific innovation or the core models and algorithms of it, can serve other industries and verticals. Herein lies the problem.
Gauging AI success in one field in many cases can be meaningless for another. To make things worse, even when trying to go deeper into the technology and attempting to evaluate, for example, which Machine Learning algorithms are utilized by the product, or what are the number of layers in the Deep Neural Network models mentioned by specific vendors, in the end it will be possibly pointless as it does not directly reflect the solution deployment ‘success’ implications.
Nevertheless, it seems that the market ignores this reality and continues to evaluate AI-based products by buzzword checklists using familiar and related AI terminology (e.g., Supervised, Unsupervised, Deep Learning etc.). While checklists are an effective tool for comparative analysis it still requires the ‘right’ items to be included. Unfortunately, what typically is absent are the items which are important to the customer, from a problem-solution perspective.
Introducing Authentic AI
Given all of this, there is a need to change the narrative around AI technology and solutions to something meaningful and authentic that reflects the real-life challenges and opportunities that businesses are facing. This is the time to introduce Authentic AI.
The Merriam-Webster dictionary defines ‘Authentic’ as both ‘worthy of acceptance or belief as conforming to or based on fact’ and ‘conforming to an original so as to reproduce essential features’. This is not about ‘Fake’ to be contrasted with ‘Real’. It’s about the essential features of AI which need to be acknowledged, and hence, redefine the ‘checklist’. Often, these essential ‘authentic’ features are hidden and only surface when a CIO/CDO is faced with a new problem to be solved. This is seen especially when the AI aspects of a proposed product or solution are fully explored by asking questions such as:
- Is the AI technology utilized by the product aimed specifically for my problem, optimally (e.g., performance, cost, etc.)?
- Is it capable of addressing the complete problem or only a part of it?
- Can it be assimilated into the existing ecosystem without imposing new demands?
- Can it address the compelling environmental conditions of the problem space?
These issues can be grouped into three different ‘classes’ – ‘Original’, ‘Holistic’ and ‘Pragmatic’:
Original – How innovative is the solution? This can be quantified by assessing the following:
- the invention of new algorithms or even new models and
- the use of complex orchestration techniques or
- through the capability to handle complex data formats and structures.
While there is no need to re-invent the wheel repetitively for any problem, there are distinctive characteristics which require optimizing.
Holistic – How complete is the proposed AI technology? It takes into account the capability of handling the end-to-end aspects of the solution, the competence of harmonizing the operation of the various AI components of the solution and the ability to adapt to ever changing conditions of the AI application.
Pragmatic – Can the technology solve real world problems in their actual and natural space in a commercially viable way? This means that for example the data sources can be processed in their most native format (both unstructured or structured) as well as provide insights or results matching the pragmatic needs of the specific market expectations. In addition, the ability to be quickly deployed and rapid to act are assessed.
All of these elements should be used to systematically assess and evaluate AI-based products and solutions to assess their authenticity and therefore effectiveness in specific use cases.
For example, many home-loan mortgage evaluation and recommendation systems utilize a somewhat isolated machine learning based applicant classification method, one of many other processes included within the solution. The AI in this solution cannot be considered Authentic AI to a high degree as it ‘scores’ low on the ‘Original’ and ‘Holistic’ classes as it isn’t innovative ‘enough’ (from an AI sense). In addition, the AI component itself does not cover on its own the end-to-end aspects of the solution (hence affecting the overall performance and precision). It could be considered to be ‘Pragmatic’ to some level if it can handle the required data sources of financial institutions or the customer applications natively, and if the solution ‘output’ are the explicit results required as a specific recommendation (e.g., loan conditions). However, the deployment timeline (time-to-market) and commercial aspects need to be evaluated as well. This is just one example of many others, covering all kinds of variations.
Perfect backflips may grant you a gold medal if you are a gymnast but if you are a master chess player don’t expect a winning move.
About the Author
Mr. Jay Klein drives Voyager Lab’s technology strategy and core intellectual property. He brings more than 25 years of experience in data analytics, networking and telecommunications to the Company. Before joining Voyager Labs, he served as CTO at Allot Communications where he steered Allot’s data inspection and analytics core technology offerings, and as VP Strategic Business Development at DSPG, where he was responsible for strategic technology acquisitions. He also co-founded and held the CTO position at Ensemble Communications while founding and creating WiMAX and IEEE 802.16. He also served as the CTO and VP of R&D at CTP Systems, acquired first by DSP Communications and later by Intel. Jay Klein holds a BSc in Electronics & Electrical Engineering from Tel Aviv University as well as numerous patents in various technology fields.