Denis Ignatovich, Co-founder and Co-CEO of Imandra, has over a decade of expertise in buying and selling, danger administration, quantitative modeling, and complicated buying and selling system design. Earlier than founding Imandra, he led the central danger buying and selling desk at Deutsche Financial institution London, the place he acknowledged the crucial function AI can play within the monetary sector. His insights throughout this time helped form Imandra’s suite of economic merchandise. Denis’ contributions to computational logic for monetary buying and selling platforms embrace a number of patents. He holds an MSc in Finance from the London Faculty of Economics and levels in Pc Science and Finance from UT Austin.
Imandra is an AI-powered reasoning engine that makes use of neurosymbolic AI to automate the verification and optimization of advanced algorithms, significantly in monetary buying and selling and software program techniques. By combining symbolic reasoning with machine studying, it enhances security, compliance, and effectivity, serving to establishments cut back danger and enhance transparency in AI-driven decision-making.
What impressed you and Dr. Grant Passmore to co-found Imandra, and the way did your backgrounds affect the imaginative and prescient for the corporate?
After school I went into quantitative buying and selling and ended up in London. Grant did his PhD in Edinburgh after which moved to Cambridge to work on purposes of automated logical reasoning for evaluation of security of autopilot techniques (advanced algorithms which contain nonlinear computation). In my work, I additionally handled advanced algorithms with a number of nonlinear computation and we realized that there’s a deep connection between these two fields. The best way that finance was creating such algorithms was actually problematic (as highlighted by many information tales coping with “algo glitches”), so we got down to change that by empowering engineers in finance with automated logical instruments to deliver rigorous scientific strategies to the software program design and improvement. Nevertheless, what we ended up creating is industry-agnostic.
Are you able to clarify what neurosymbolic AI is and the way it differs from conventional AI approaches?
The sphere of AI has (very roughly!) two areas: statistical (which incorporates LLMs) and symbolic (aka automated reasoning). Statistical AI is unimaginable at figuring out patterns and doing translation utilizing info it discovered from the info it was educated on. However, it’s dangerous at logical reasoning. The symbolic AI is nearly the precise reverse – it forces you to be very exact (mathematically) with what you’re making an attempt to do, however it will probably use logic to motive in a approach that’s (1) logically constant and (2) doesn’t require knowledge for coaching. The strategies combining these two areas of AI are referred to as “neurosymbolic”. One well-known utility of this method is the AlphaFold mission from DeepMind which lately gained the Nobel prize.
What do you assume units Imandra aside in main the neurosymbolic AI revolution?
There are numerous unimaginable symbolic reasoners on the market (most in academia) that concentrate on particular niches (e.g. protein folding), however Imandra empowers builders to investigate algorithms with unprecedented automation which has a lot higher purposes and higher goal audiences than these instruments.
How does Imandra’s automated reasoning remove widespread AI challenges, reminiscent of hallucinations, and enhance belief in AI techniques?
With our method, LLMs are used to translate people’ requests into formal logic which is then analyzed by the reasoning engine with full logical audit path. Whereas translation errors could happen when utilizing the LLM, the person is supplied with a logical clarification of how the inputs had been translated and the logical audits could also be verified by third get together open supply software program. Our final aim is to deliver actionable transparency, the place the AI techniques can clarify their reasoning in a approach that’s independently logically verifiable.
Imandra is utilized by Goldman Sachs and DARPA, amongst others. Are you able to share a real-world instance of how your expertise solved a posh downside?
An amazing public instance of the true world influence of Imandra is highlighted in our UBS Way forward for Finance competitors 1st place win (the main points with Imandra code is on our web site). Whereas making a case research for UBS that encoded a regulatory doc that they submitted to the SEC, Imandra recognized a basic and refined flaw within the algorithm description. The flaw stemmed from refined logical circumstances that should be met to rank orders inside an order guide – one thing that will be inconceivable for people to detect “by hand”. The financial institution awarded us 1st place (out of greater than 620 firms globally).
How has your expertise at Deutsche Financial institution formed Imandra’s purposes in monetary techniques, and what’s probably the most impactful use case you have seen to this point?
At Deutsche Financial institution we handled quite a lot of very advanced code that made automated buying and selling choices primarily based on varied ML inputs, danger indicators, and so forth. As any financial institution, we additionally needed to abide by quite a few rules. What Grant and I noticed was that this, on a mathematical degree, was similar to the analysis he was doing for autopilot security.
Past finance, which industries do you see as having the best potential to profit from neurosymbolic AI?
We’ve seen AlphaFold get the Nobel prize, so let’s positively depend that one… Finally, most purposes of AI will tremendously profit by use of symbolic strategies, however particularly, we’re engaged on the next brokers that we’ll launch quickly: code evaluation (translating supply code into mathematical fashions), creating rigorous fashions from English-prose specs, reasoning about SysML fashions (language used to explain techniques in safety-critical industries) and enterprise course of automation.
Imandra’s area decomposition is a novel function. Are you able to clarify the way it works and its significance in fixing advanced issues?
A query that each engineer thinks about when writing software program is “what the sting instances?”. When their job is QA and they should write unit take a look at instances or they’re writing code and desirous about whether or not they’ve accurately carried out the necessities. Imandra brings scientific rigor to reply this query – it treats the code as a mathematical mannequin and symbolically analyzes all of its edge instances (whereas producing a proof in regards to the completeness of protection). This function relies on a mathematical method referred to as ‘Cylindrical Algebraic Decomposition’, which we’ve “lifted” to algorithms at giant. It has saved numerous hours for our prospects in finance and uncovered crucial errors. Now we’re bringing this function to engineers in every single place.
How does Imandra combine with giant language fashions, and what new capabilities does this unlock for generative AI?
LLMs and Imandra work collectively to formalize human enter (whether or not it’s supply code, English prose, and so forth), motive about it after which return the output in a approach that’s straightforward to grasp. We use agentic frameworks (e.g. Langgraph) to orchestrate this work and ship the expertise as an agent that our prospects can use instantly, or combine into their purposes or brokers. This symbiotic workflow addresses lots of the challenges of utilizing LLM-only AI instruments and extends their utility past beforehand seen coaching knowledge.
What’s your long-term imaginative and prescient for Imandra, and the way do you see it reworking AI purposes throughout industries?
We expect neurosymbolic strategies would be the basis that paves the best way for us to understand the promise of AI. Symbolic strategies are the lacking ingredient for many of the industrial purposes of AI and we’re excited to be on the forefront of this subsequent chapter of AI.
Thanks for the nice interview, readers who want to be taught extra ought to go to Imandra.
