Artificial Intelligence
Navigating Data Collection in the Age of AI
By Kate Huber

You can have your data cake and eat it, too!

You can’t escape the mounting stories about AI:

Rumor: AI is replacing jobs, leading to mass unemployment.

Reality: Not likely in the foreseeable future, not even for jobs that are increasingly mechanized.

Rumor: AI is curing diseases.

Reality: AI needs to master accurate disease diagnosis and drug delivery first.

Rumor: AI is writing business proposals.

Reality: AI can provide pointers and a sensible outline if given the right information, but won’t be writing long documents like white papers or technical documentation by itself.

AI and the importance of data training

For AI to succeed, data scientists need to feed it copious amounts of good, healthy data. That’s where robust data collection comes in—smart, intelligent data collection in modern business operations will help you build sound data pipelines throughout your organization.

But how do you get quality data? You train it!

Right now, AI is similar to a toddler: it can only repeat what it has been taught. AI solely gathers and consolidates data from the limited sources you’ve provided. Consequently, AI has often led to what are called "AI hallucinations"—essentially imaginary outcomes because it cannot always correctly interpret the data it has gathered. Some recent examples of AI hallucinations include:

  • Lawyers presented a ChatGPT-generated legal brief to a judge, which included fake quotes and non-existent cases.
  • Google Bard stated that the James Webb Telescope took the first picture of a planet outside the Milky Way, when in fact the first picture was taken well before JWTS was launched.
  • The Chronicles of Higher Education provided a university professor with fake reference sources because it understands what a reference should resemble.

To train your data, data scientists need to take your raw data and normalize it by:

  • Reducing its size to make it manageable.
  • Removing any extraneous information.
  • Formatting it so your AI can read it.

Collecting data is like baking a cake – the quality of ingredients is paramount!

The most important aspect of data collection for your AI is the quality of your data. The data entering your organization—and the parameters on which it's trained—are what will ensure your AI's results are accurate. After all, your data forms the basis of your AI's knowledge. If the data hasn’t been cleaned and vetted, your AI will stumble, and its output will be subpar.

Imagine your organization’s data center as a delicious cake that your customers can’t wait to slice into. The baker, your data scientist, needs the best ingredients to succeed:

  1. Quality ingredients: Prioritize high-quality data as the primary ingredient for your AI solutions. This could involve not only gathering and sifting through a large volume of data but also ensuring that the data is relevant, diverse, and representative of the problem domain.
  2. Cooking device: An oven made of hardware and software for developing and deploying AI models, including powerful servers equipped with specialized AI chips, advanced software frameworks, and tools for machine learning (ML) and deep learning (DL).
  3. Recipe: Industry best practices (recipes) and methodologies for developing AI solutions that involve a systematic approach to data collection, preprocessing, model selection, training, evaluation, and deployment. Proprietary techniques or algorithms tailored to specific use cases or industries can also be employed.

Like the recipe above, Concord follows a rigorous process when collecting data to ensure quality and relevance:

  1. Strategic planning: Plans the data collection process, identifies the sources and types of data needed to address the problem at hand.
  2. Data sourcing: Leverages a combination of internal data sources, third-party data providers, and public datasets to gather the necessary information.
  3. Data quality assurance: Implements robust data quality assurance measures to ensure that the collected data is accurate, reliable, and free from errors or biases.
  4. Ethical considerations: Adheres to ethical guidelines and regulations governing data collection, ensuring that privacy and security concerns are addressed appropriately.
  5. Documentation and versioning: Maintains comprehensive documentation of the collected data, including metadata and versioning information, to facilitate reproducibility and transparency in its AI projects.

Concord approaches data collection as a critical component of its AI development process, recognizing the importance of sourcing high-quality data to achieve successful outcomes for its clients and stakeholders.

Interested in learning more about how our team is using AI to revolutionize data collection? Pick up that fork and dig into that data cake with Concord!

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