5 Inconvenient Questions when hiring an AI company

Artificial intelligence (AI) is rapidly becoming an indispensable part of many businesses. However, it is important to ask some tough questions before hiring an AI company. For example, if you are not aware of the underlying AI models that are being used in a product, you may not be able to anticipate its limitations. Additionally, if you are not sure how your data will be handled, you may be putting your company at risk. This blog provides some advice on how to test AI systems to make sure that they meet your needs.

 

  1. Which concrete AI models do you use in your product? Who is their owner, and what are their license terms?
    Providers of AI-products can’t keep these things in secret, since each underlying model has its own specific constraints and terms of use. And you, as a customer, must perform a background check to avoid any surprises.
    For example, LLama2 (and derivatives) could be used only with English language, while ChatGPT can have degradedavailability during the peak hours.
     
  2. Can you guarantee that 3rd parties will not see my data?
    For example, if GPT/LLM models are provided by Microsoft Azure, then all requests are stored there for 30 days. An exception can be made for the customers that have gone through the special approval process.
    Of course, it is better when all models run on the premises under your control completely.
     
  3. You are offering data anonymisation for GDPR compliance. Can you guarantee (in writing) that your process is 100% efficient? Better yet, can you demo the process to us on our data?
    It is fairly easy to promise data anonymisation. However, it is nearly impossible to ensure 100% removal of Personally Identifiable Information (PII), especially from the unstructured data. Automated processes can miss some nuances that are specific to your business.
    Best approach here is to talk through the nuances and evaluate data anonymisation of your own data.
     
  4. Do you offer AI-driven information retrieval?
    Let’s test your system on our data together! Our employees will gather a a bunch of documents for 1000 pages and will prepare a list of 20 questions. 10 of these questions will have answers on specific pages, while 10 questions will have no answers.
    This document package could be prepared by your own employees, the ones that will be using the new system on a day-to-day basis. Nobody would also be better for coming up with a list of domain-specific questions.
    To keep the test fair, make sure that the provider of AI-driven product does not see the documents and questions before the test.
     
  5. For an AI system, it is fine to make mistakes. Even OpenAI ChatGPT makes them and learns from them. But how can your system capture user feedback and learn from it? Demo this to us!
    You will have to work with the new AI product for many years. This cooperation will be more valuable for you, if the system could not only adapt to your business, but also adapt to any future changes.

These 5 questions will be very inconvenient for the providers of AI-driven products that try to sell thin wrappers around ChatGPT or LLamaIndex as a “GDPR-compliant technological breakthrough”. Vendors of AI products that have invested a lot of thought, expertise and effort into their products - would be delighted to talk about these topics.

This also includes the TIMETOACT GROUP. If you are interviewing AI products for joining your business, consider inviting us over, too!

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So You are Building an AI Assistant?

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Common Mistakes in the Development of AI Assistants

How fortunate that people make mistakes: because we can learn from them and improve. We have closely observed how companies around the world have implemented AI assistants in recent months and have, unfortunately, often seen them fail. We would like to share with you how these failures occurred and what can be learned from them for future projects: So that AI assistants can be implemented more successfully in the future!

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8 tips for developing AI assistants

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Let's build an Enterprise AI Assistant

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Part 3: How to Analyze a Database File with GPT-3.5

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Part 1: Data Analysis with ChatGPT

In this new blog series we will give you an overview of how to analyze and visualize data, create code manually and how to make ChatGPT work effectively. Part 1 deals with the following: In the data-driven era, businesses and organizations are constantly seeking ways to extract meaningful insights from their data. One powerful tool that can facilitate this process is ChatGPT, a state-of-the-art natural language processing model developed by OpenAI. In Part 1 pf this blog, we'll explore the proper usage of data analysis with ChatGPT and how it can help you make the most of your data.

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Creating a Social Media Posts Generator Website with ChatGPT

Using the GPT-3-turbo and DALL-E models in Node.js to create a social post generator for a fictional product can be really helpful. The author uses ChatGPT to create an API that utilizes the openai library for Node.js., a Vue component with an input for the title and message of the post. This article provides step-by-step instructions for setting up the project and includes links to the code repository.

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The Intersection of AI and Voice Manipulation

The advent of Artificial Intelligence (AI) in text-to-speech (TTS) technologies has revolutionized the way we interact with written content. Natural Readers, standing at the forefront of this innovation, offers a comprehensive suite of features designed to cater to a broad spectrum of needs, from personal leisure to educational support and commercial use. As we delve into the capabilities of Natural Readers, it's crucial to explore both the advantages it brings to the table and the ethical considerations surrounding voice manipulation in TTS technologies.

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GenAI Consulting

ChatGPT, Bard & Co. have shown at the latest: Generative AI has the potential to revolutionize the world of work. With GenAI Consulting, we support you in exploiting this potential for your company.

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Operationalization of Data Science (MLOps)

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Part 4: Save Time and Analyze the Database File

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LLM Performance Series: Batching

Beginning with the September Trustbit LLM Benchmarks, we are now giving particular focus to a range of enterprise workloads. These encompass the kinds of tasks associated with Large Language Models that are frequently encountered in the context of large-scale business digitalization.

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The amount of data that companies produce and process every day is constantly growing. This data contains valuable information about customers, markets, business processes and much more. But how can companies use this data effectively to make better decisions, improve their products and services and tap into new business opportunities?

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Part 2: Data Analysis with powerful Python

Analyzing and visualizing data from a SQLite database in Python can be a powerful way to gain insights and present your findings. In Part 2 of this blog series, we will walk you through the steps to retrieve data from a SQLite database file named gold.db and display it in the form of a chart using Python. We'll use some essential tools and libraries for this task.

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IBM Watson Knowledge Studio

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Data Science & Advanced Analytics includes a wide range of tools that can examine business processes, help drive change and improvement.

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Data Integration, ETL and Data Virtualization

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Introduction to Partial Function Application in F#

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IBM Cloud Pak for Data Accelerator

For a quick start in certain use cases, specifically for certain business areas or industries, IBM offers so-called accelerators based on the "Cloud Pak for Data" solution, which serve as a template for project development and can thus significantly accelerate the implementation of these use cases. The platform itself provides all the necessary functions for all types of analytics projects, and the accelerators provide the respective content.

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Tracing IO in .NET Core

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Introduction to Functional Programming in F# – Part 4

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Business Intelligence

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Introduction to Functional Programming in F# – Part 8

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IBM Watson Discovery

With Watson Discovery, company data is searched using modern AI to extract information. On the one hand, the AI uses already trained methods to understand texts; on the other hand, it is constantly developed through new training on the company data, its structure and content, thus constantly improving the search results.

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Introduction to Functional Programming in F# – Part 2

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Introduction to Functional Programming in F# – Part 10

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Machine Learning Pipelines

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Introduction to Functional Programming in F# – Part 11

Learn type inference and generic functions in F#. Boost efficiency and flexibility in your code with these essential programming concepts.

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The Power of Event Sourcing

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Cloud from Atlassian

Atlassian Cloud gives even small teams the ability to leverage the Atlassian product world. It allows you to flexibly adjust the number of users. Furthermore, you can choose from three paid variants of Cloud products. In this way, you always adapt your Atlassian product to your individual requirements. The multiple certified Atlassian Cloud solutions are hosted at Amazon Web Services Inc.

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Introduction to Functional Programming in F# – Part 6

Learn error handling in F# with option types. Improve code reliability using F#'s powerful error-handling techniques.

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Introduction to Functional Programming in F# – Part 7

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Designing and Running a Workshop series: The board

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ADRs as a Tool to Build Empowered Teams

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Dashboards & Reports

The discipline of Business Intelligence provides the necessary means for accessing data. In addition, various methods have developed that help to transport information to the end user through various technologies.

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Acer Hardware

Acer's Chrome hardware makes the move to the Cloud even easier for you and your colleagues. The products are simple, elegant and powerful.

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IBM Watson® Knowledge Catalog/Information Governance Catalog

Today, "IGC" is a proprietary enterprise cataloging and metadata management solution that is the foundation of all an organization's efforts to comply with rules and regulations or document analytical assets.

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Designing and Running a Workshop series: An outline

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Using NLP libraries for post-processing

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Revolutionizing the Logistics Industry

As the logistics industry becomes increasingly complex, businesses need innovative solutions to manage the challenges of supply chain management, trucking, and delivery. With competitors investing in cutting-edge research and development, it is vital for companies to stay ahead of the curve and embrace the latest technologies to remain competitive. That is why we introduce the TIMETOACT Logistics Simulator Framework, a revolutionary tool for creating a digital twin of your logistics operation.

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Part 1: Detecting Truck Parking Lots on Satellite Images

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So, I wrote a book

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IBM InfoSphere Information Server

IBM Information Server is a central platform for enterprise-wide information integration. With IBM Information Server, business information can be extracted, consolidated and merged from a wide variety of sources.

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IBM Watson Assistant

Watson Assistant identifies intention in requests that can be received via multiple channels. Watson Assistant is trained based on real-live requests and can understand the context and intent of the query based on the acting AI. Extensive search queries are routed to Watson Discovery and seamlessly embedded into the search result.

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Running Hybrid Workshops

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My Workflows During the Quarantine

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Building A Shell Application for Micro Frontends | Part 4

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Consistency and Aggregates in Event Sourcing

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Composite UI with Design System and Micro Frontends

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Talend Data Integration

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Creating a Cross-Domain Capable ML Pipeline

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Boosting speed of scikit-learn regression algorithms

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Event Sourcing with Apache Kafka

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Building and Publishing Design Systems | Part 2

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Innovation Incubator Round 1

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Part 2: Detecting Truck Parking Lots on Satellite Images

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State of Fast Feedback in Data Science Projects

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Inbox helps to clear the mind

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Data Insights

With Data Insights, we help you step by step with the appropriate architecture to use new technologies and develop a data-driven corporate culture: from the development of new data sources, to exploratory analysis to gain new insights, to predictive models.

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AUTOPILOT Economy Class

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