Sound AI Startup Guide

Overview

Artificial intelligence (AI) methods and processes fundamentally transform the economy and society. The potential future business potential of AI startups is correspondingly high. It is to be expected that many of them will turn out to be “unicorns”.

Thus it is very important for venture capitalists, banks & insurances to deal timely with often capital-intensive AI startups.
However, the procedures in use are new, complex, and often intransparent. It is of correspondingly high importance to be able to judge their correctness.

We have long-year experience in the areas Deep Learning / Artificial Intelligence and have supported well-known banks and insurances in these topics.
On this basis, we have developed a guide enabling the efficient pre-audit of AI start-ups concerning the most relevant topics.
With that, its is possible to assess the solidity of AI start-ups in an effective questionnaire-based way and to identify potentially critical issues.
Risks can be mitigated and losses from “wrong“ engagements correspondingly be reduced.

Transformative Developments

The processes known as “artificial intelligence” (“AI”) are already in use in many ways and transform the economy and society in a fundamental way. Their fields of application are as diverse as they are promising for the future and include, among others

  • recommendation systems for customer-specific products
  • autonomous driving for cars and drones
  • automated recognition of clinical pictures
  • automated high quality translation services

Despite progress already made, the number and quality of new developments remains strong and the number of start-ups founded for this purpose continues to be high.

High Complexity

Though the components often available “out-of-the-box”, the complexity of AI procedures is generally very high and requires relevant expertise. Thus, e.g., it is imperative to take care

  • which AI model to use
  • how to select and prepare the data
  • how to ensure that potentially self-generated data leads to realistic results
  • how to ensure that the quality of the results is sufficient

This is made more difficult by the fact that many procedures are ultimately “black boxes” and are difficult to validate.

In addition, some special features must also be taken into account in the implementation and the chosen infrastructure (like the use of GPUs, Cloud solutions, etc).

Potential Issues

The necessity to enter into fast engagements in order not to get left behind and the at the same time high complexity of the issue means high risks for venture capitalists as well as for banks and insurance companies. These are reinforced by the facts that, e.g.

  • many startups take advantage of the hype and (let) call themselves “AI startups”, although this is not always the case. (According to a study 40% of all European “AI start-ups” have nothing to do with AI.)
  • many procedures are still very new and their effectiveness is doubtful
  • complex AI procedures are often used, although they are not necessary and/or the data situation is not sufficient; often “classical Machine Learning” is preferable to Deep Learning
  • many founders are very inexperienced and therefore initiate fragile processes
  • unauthorized data or at least data from sources that are not bound by contract to provide these data is used

For potential investors and contractual partners, there is thus a considerable risk of possible misinvestments and unrecognised financial losses as well as damage to their reputation.

Our Range of Services

We bring in our guide, which covers the following relevant topics

  • data for training, testing & production
  • methodology use and validation
  • processes in place
  • used systems and hardware
  • license situation

For each of these topics, we have identified relevant issues for which we provide a list of questions and risks as well as short descriptions.

In addition, we can introduce the guide in the scope of a workshop and support in overall questions concerning AI / Machine Learning / Deep Learning.

If desired, we also offer advice on special cases and support you in identifying possible risks and formulating and evaluating questions to be asked.

Your Benefits

  • insight into the critical points and the questions to be asked regarding AI start-ups
  • effective prior clarification of critical points
  • identification of start-ups where commitment is out of question
  • use of freed-up time resources to review more appropriate cases
  • make quicker decisions and stay connected to promising technologies
  • if required, in-depth analysis of specific start-up in question
  • reduction of risks of bad investments and damage to reputation
  • saving of money and resources

Depending on customer requirements, a flexible procedure is possible.

You are welcome to contact us, preferably via email.

Contact

Dr. Dimitrios Geromichalos
Founder / CEO
RiskDataScience UG (haftungsbeschränkt)
Theresienhöhe 28, 80339 München
E-Mail: riskdatascience@web.de
Telefon: +4989244407277, Fax: +4989244407001
Twitter: @riskdatascience

AI as a Module – A New Kind of Service

Motivation and Concept

In order to meet the customers’ need for effective solutions in the field of Artificial Intelligence, various approaches – and combinations of them – are possible

  • Consulting: the complete development takes place on site at the customer, who also receives all rights
  • Software Development (“Software”): the customer aquires licences of a product and operates it on his own infrastructure
  • Software as a Service (SaaS): the customer aquires licences of a product that is operated on the remote infrastructure of the SaaS provider

For the customers, each of these approaches has specific advantages and disadvantages.

Today’s technological possibilities make a further prototypical approach – AI as a Module (AIaaM) – possible

  • the client acquires a pre-trained model (such as neural network) for specific tasks (such as translation of texts), which he operates independently
  • the model is not a software in the narrow sense, but requires e.g. a Python environment. The procedures and data for the training remain with the supplier

As shown below, this approach combines several advantages of consulting, software development, and SaaS for customers.

Advantages and Disadvantages from the Customer Point of View

For the customers, each of the mentioned service types has specific pros and cons

  • Consulting
    • Pros
      • Very high flexibility: Consulting projects are extremely tailored to customer needs
      • Know-how build-up: Customers can acquire knowledge from the consultants and use it for further tasks
      • Contact persons: There are contacts available for every requirement at least throughout the project
    • Cons
      • Time comsumption: Consulting projects are often extremely time-demanding and can take up to years
      • Expensiveness: Correspondingly, the costs are also very high – and can even continue to rise
  • Software
    • Pros
      • Low price: In general, software is relatively cheap – in some cases even free
      • Standardization: Software for specific tasks is often standardized and allows the customer to use best practice approaches
    • Cons
      • “Black box“: Often, customers have no possibility to examine how the used procedures in the software really work. (This is not the case for Open Source software, of course.)
      • Lengthy approval processes: Companies in regulated industries – like banks – have often lengthy approval processes in place. The effort can even reach proportions of projects.
      • Security risks: Complex software may have unknown security vulnerabilities, and open e.g. the door for hackers
  • SaaS
    • Pros
      • Low price: In general, SaaS is relatively cheap
      • Resource saving: Customers need only very limited resources for operating the service
    • Cons
      • “Black box“: As with software, customers often have no possibility to examine how the used procedures of the service really work
      • Supplier dependency: Customers depend on the service of third parties and are directly affected, e.g. in the case of an insolvency
      • Hurdles due to outsourcing: SaaS is often seen as a form of outsourcing. Depending on industry and legislation, legal hurdles can arise
  • AIaaM
    • Pros
      • High flexibility: Pre-trained models can be used extremely flexible inside an organization’s processes. E.g., a translator module can easily be a connected upstream of a classification routine
      • Build-up of relevant know-how: Customers can learn how to apply the modules with own, customized procedures
      • Lower regulatory hurdles: Since AIaaM is no full software in the narrow sense, hurdles should be significantly lower
      • Transparent tools: Customers can integrate the modules into their own and maintain an overall transparency
      • Low price: The price of a module is generally even lower than that of a commercial software since there is no there is no superstructure.
      • Efficiency: Customers are not forced to purchase unnecessary features that are already covered by other tools
    • Cons
      • No access to training procedures: Customers just acquire the trained model and not the training data or the training procedures. This is not necessarily a disadvantage, however, if only limited resources are available and the core business is a different one
      • Medium implementation effort: The installation needs some minimum programming, e.g. in Python. The knowledge for that should be available in each medium or large organization, however

In summary, AaaM combines several advantages of consulting, software, and SaaS – and avoids most disadvantages.

We are happy to support our customers with related issues.

Contact

Dr. Dimitrios Geromichalos
Founder / CEO
RiskDataScience UG (haftungsbeschränkt)
Theresienhöhe 28, 80339 München
E-Mail: riskdatascience@web.de
Telefon: +4989244407277, Fax: +4989244407001
Twitter: @riskdatascience