AI

Businesses Emphasize Importance of Generative AI Education and Strategic Mindset

John Abel from Google Cloud stresses the importance of educating business staff on generative AI as a tool that requires training and investment, while Microsoft’s Orca-Math, a small language model, showcases advancements in AI technology for diverse business needs.

At a glance

  • Generative AI should be viewed as a tool that requires training and investment, not just a tech project.
  • Businesses implementing generative AI should focus on coaching models and creating systems knowledge experts.
  • The global AI market is expected to reach $267 billion by 2027, making AI a highly sought-after technology.
  • The Strategic Mindset Framework outlines ways to engage with customers and integrate AI processes.
  • Microsoft’s Orca-Math, a small language model, outperforms larger models in solving math problems and showcases advancements in AI technology.

The details

Google Cloud’s John Abel recently emphasized the importance of educating business staff on generative AI, stating that it should not be viewed solely as a tech project but as a tool that requires training and investment.

He highlighted the need for businesses implementing generative AI to define their AI strategy externally and see it as coaching the models, not just training them.

Abel also stressed the importance of creating systems knowledge experts rather than just chatbots when implementing generative AI. It is crucial for businesses to document and understand error levels in generative AI systems, avoid overthinking projects, and focus on finding tools that best suit their needs.

The global AI market

is expected to reach $267 billion by 2027, making AI a highly sought-after digital technology for businesses.

The strategic mindset of leaders plays a significant role in influencing AI decision-making, as they rely on simplified mindsets when facing complex problems.

Enterprises should consider what mindset governs their strategic decision-making for AI use and align it with customer preferences for effective engagement.

The Strategic Mindset Framework

outlines four ways of engaging with customers, emphasizing the need to bridge silos, democratize data access, and reskill employees to integrate AI processes fully.

Microsoft recently unveiled a small language model named Orca-Math, which boasts seven billion parameters and outperforms larger models like GPT 3.5 in solving grade school math problems.

The model, a fine-tuned version of Mistral 7B, was trained on 200,000 synthetically generated math problems.

Orca-Math has shown impressive performance on benchmarks like the GSM8K, scoring 86.81% on the pass@1 metric and excelling on various math datasets.

Financial and manufacturing firms can leverage AI models with math reasoning capabilities for diverse business needs, with the dataset used to build Orca-Math available for fine-tuning commercial applications.

Microsoft’s research team argues that smaller models and datasets allow for faster and cheaper training, highlighting the potential for AI advancements in various sectors.

While it remains unclear if Orca-Math will be made publicly available, the dataset can be utilized to enhance math reasoning skills in existing open-source models.

Microsoft researchers have been driving innovation in the small language model space, with models like Phi-2 and the Orca series showcasing significant advancements in AI technology.

In conclusion, the integration of generative AI, strategic mindset frameworks for AI decision-making, and the development of advanced language models like Orca-Math signal a transformative shift in the application of AI technologies across industries.

This emphasizes the need for continuous learning and adaptation to leverage the full potential of AI in business operations.

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Facts attribution

This section links each of the article’s facts back to its original source.

If you suspect false information in the article, you can use this section to investigate where it came from.

aibusiness.com
– John Abel from Google Cloud emphasized the importance of educating business staff on generative AI
– Generative AI should not be viewed solely as a tech project, but as a tool that requires training and investment
– Businesses implementing generative AI should define their AI strategy externally, not just internally
– Generative AI should be seen as coaching the models, not just training them
– Systems knowledge experts should be created, rather than just chatbots, when implementing generative AI
– Error levels in generative AI systems should be well documented and understood by businesses
– Businesses should avoid overthinking projects and focus on finding tools that best suit their needs
aibusiness.com
– AI is a highly sought-after digital technology for businesses
– Global AI market expected to reach $267 billion by 2027
– Strategic mindset of leaders influences AI decision-making
– Leaders rely on simplified mindsets when facing complex problems
– Enterprises should consider what mindset governs their strategic decision-making for AI use
– Strategic Mindset Framework depicts four ways of engaging with customers
– Enterprises often have an overarching mindset that frames their customer engagement strategy
– Leaders should be aware of how their mindset limits their ability to use AI in new ways
– AI can be used to automate marketing and sales operations
– AI can help enterprises listen to customers and learn about their needs
– AI can be used to create unique advantages with value propositions
– AI can be used to facilitate co-creative and crowd-sourced business-with-customer engagement
– AI can be used to empower customers and help them make smarter choices
– AI can be used to engage customers in socio-political issues
– AI can be used to increase educational training and entertainment through AR and VR
– AI can be used to settle claims faster and engage customers in charity programs
– Enterprises should align their mindset with customer preferences for effective AI engagement
– Enterprises need to bridge silos, democratize data access, and reskill employees to fully integrate AI processes
– Customers demand more personal and targeted engagement from enterprises
– Leaders should critically reflect on which mindset is most opportune for effective customer engagement with AI
– IT investments can be wasted if the optimal mindset for customer engagement is not identified
– Strategic mindsets are not mutually exclusive and can be combined for successful AI-enabled customer engagement
– Leadership teams with multiple strategic mindsets can create internal inefficiencies and risks
– Mindset awareness enables better leadership for gaining competitive advantage through effective customer engagement with AI
aibusiness.com
– Microsoft has unveiled a small language model called Orca-Math that can solve math problems better than models 10 times its size
– Orca-Math has seven billion parameters and outperforms models like GPT 3.5, Gemini Pro, and Llama 2 70B in solving grade school math problems
– The model is a fine-tuned version of Mistral 7B and was trained on 200,000 synthetically generated math problems
– Microsoft’s research team argues that smaller models and datasets allow for faster and cheaper training
– Financial and manufacturing firms can apply AI models with math reasoning capabilities for various business needs
– Orca-Math scored 86.81% on the pass@1 metric on the GSM8K benchmark, outperforming larger systems like Llama 2, WizardMath-70B, and GPT-3.5
– Orca-Math also performed well on other math datasets like AddSub, MultiArith, and SinglEq
– Researchers used an iterative learning process in addition to fine-tuning to improve Orca-Math’s performance
– Microsoft researchers have been making strides in the small language model space with models like Phi-2 and the Orca series
– The dataset used to build Orca-Math is available via Hugging Face for fine-tuning commercial applications
– It is unclear if Orca-Math will be made available to the public, but the dataset can be used to improve math reasoning skills in existing open source models like Llama 2 or Bloomz

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