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Artificial Intelligence

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A new neuromorphic chip architecture points to faster, more energy-efficient AI.  IBM North Pole

This paper explains that there is a strong need for designing energy-efficient AI computers. It describes a chip with a neural inspired architecture, IBM calls NorthPole, that achieves substantially higher performance, energy efficiency, and area efficiency compared with other comparable architectures.

IBM NorthPole CPU

Inspired by the organic brain and optimized for inorganic silicon, NorthPole is a neural inference architecture that blurs this boundary by eliminating off-chip memory, intertwining compute with memory on-chip, and appearing externally as an active memory chip. NorthPole is a low-precision, massively parallel, densely interconnected, energy-efficient, and spatial computing architecture with a co-optimized, high-utilization programming model.

The paper compares NorthPole with dozens of other AI chips including chips from Intel, Nvidia, Google, Qualcomm, Amazon, Applied Brain Research, and Baidu.  Advanced AI chips use neuromorphic architectures.  The moving and shuffling of data takes a lot of energy.  In neuromorphic architectures the memory elements are intertwined with the processing elements at a very fine scale.  This decentralized memory model along with high data parallelism are key factors for greater energy efficiency.  NorthPole is about 5 times faster and more energy efficient than the Nvidia H100.  For more details, check out this YouTube video from Anastasi In Tech.

TensorsData may be organized in a multidimensional array that is referred to as a "data tensor"; however in the strict mathematical sense, a tensor is a multilinear mapping over a set of domain vector spaces to a range vector space. Observations, such as images, movies, volumes, sounds, and relationships among words and concepts, stored in a data tensor array may be analyzed by artificial neural network tensor methods.  Computations involve matrix representations of linear transformations, calculating the null space and range of  linear transformations, and the rank of linear transformations.  This linear algebra article shows the math in a simple example.  Here is an image processing example:

AI example

Since 2020, OpenAI has developed its generative artificial intelligence technologies on a massive supercomputer constructed by Microsoft, one of its largest backers, that uses 10,000 of Nvidia's graphics processing units (GPUs).  An effort to develop its own AI chips would put OpenAI among a small group of large tech players such as Google and, that have sought to take control over designing the chips that are fundamental to their businesses.

It is not clear whether OpenAI will move ahead with a plan to build a custom chip.  An acquisition of a chip company could speed the process of building OpenAI’s own chip - as it did for and its acquisition of Annapurna Labs in 2015.

Building an AI-Friendly Culture

  • Practice active listening
  • Emphasize the importance of your people
  • Share the vision

AI and the Organizational Structure

  • AI will automate operational tasks
  • Report generation and project tracking can be automated with AI
  • Flatter, team-based structures will drive innovation

Lessons in AI Implementation

  • Walmart implemented AI across the business, from inventory management to customer service
  • IKEA uses AI for routine customer inquires
  • Bank of America uses AI to monitor transactions
    • Start with the problem
    • Avoid layering new tech onto old processses
    • Involve end users early and often
  • Genpact

AI and Your Workforce

  • AI and employee retention
    • Provides real-time feedback
    • Tracks rewards and recognition
    • Foster work-life balance
    • Automates routine tasks
  • AI and employee development
  • AI and performance management
    • Analyze multiple data sources
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    • Streamline roles and responsibilities
  • AI and team effectiveness
    • AI augments our ability to work together and boosts communication
    • Create transparancy and accountability
    • Balance workloads
    • Create positive teams

AI and Business Value

  • AI and business differentiation
  • Mitigate AI risks
    • Data classification framework
    • Use acronyms and mnumonics
    • Use diverse datasets
    • Implement data governance
    • Proactive training
  • Ethics and Fairness
    • Ensure transparancy
      • Test regularly
      • Update models
      • Audit
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    • Prioritize privacy
    • The right talent
      • legal
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Leadership in the age of AI

Just finished the course “AI Challenges and Opportunities for Leadership” by Conor Grennan

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