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PBL can be transformative for students — especially those furthest from educational opportunity. Read more stories. Learn about partnerships.

The Great Learning: Social Acceptance, A Challenge An Awakening Wild Woman Faces

View the projects. Get the planner. And more resources to support Project Based Learning.


  1. Raven Hill.
  2. Part 1: Every Child Is a Genius.
  3. The Changes That Take Place When You Start Your Period.
  4. An ASCD Study Guide for Awakening Genius in the Classroom.
  5. Psychology in Everyday Life.

See more resources. Project Based Learning for all.


  • Psychology in Everyday Life.
  • The Asphalt Warrior?
  • Marvins Curse?
  • Post navigation.
  • Why PBL? See the evidence and impact of PBL below Student Impact A great project engages students in deeper learning. See the impact.

    Great Awakening, 1st Edition | Macmillan Learning for Instructors

    Explore the research. Our exclusive focus is supporting educators like you with quality PBL professional development.

    Are you ready to move from theory into practice with Project Based Learning? Deep learning is a subset of machine-learning methods based on learning data representations, as opposed to task-specific algorithms. In the past, AI used hand-coded "rules" to make decisions. This had very limited success.

    Psychology in Everyday Life

    Source: Nvidia, FMS A breakthrough came five years ago when deep-learning techniques using deep neural networks, or DNN were implemented. Google Translate, for one, became much more accurate when Google deployed a DNN platform last year, yielding more improvements than in the previously 10 years combined. Moreover, as shown in Figure 1 , image recognition improved dramatically when DNNs have been applied versus previous hand-coded techniques. DNN involves training machines with vast amounts of data. Once trained, an AI system is able to make better predictions based on new input.

    This is why much greater memory bandwidth is needed for AI. But the data-hungry, multicore nature of the processing units needed for machine learning requires even greater memory bandwidth to feed the processing cores with data. This is where high bandwidth memory HBM is beginning to make a critical contribution that will grow by orders of magnitude over the next several years. These stacks are known as memory "cubes," each of which can have up to eight DRAM die. This will improve system performance and increase energy efficiency, enhancing the overall effectiveness of data-intensive, high-volume applications that depend on machine learning, parallel computing,and graphics rendering.

    Shown is how a high-bandwidth-memory structure connects to processor.

    Ho`āla Foundation for Education

    Source: Samsung. Figure 2 provides an example of an HBM structure, as well as how it connects to a processor.

    Part I: Learning Machine

    HBM is continuing to rapidly evolve to meet the needs of machine learning. The technology has already gained significant traction in the market, going from virtually no revenue a couple of years ago to what will likely be billions of dollars over the next few years. Not only is HBM being used to increase the speed at which local data is fed to processors, designers are also looking at ways to leverage it to speed movement of data between computing systems. HBM2 is being designed into high-end switching ASICs application-specific integrated circuits that can operate at terabit speeds, to create a significant edge in routing performance.

    On top of that, new breeds of data-center ASICs used in servers are leveraging HBM, suggesting routing capability much closer to the optimum AI compute node than traditional networks. Expect smarter, faster networks in the near future that will play a pivotal role in the life-enhancing expansion of machine intelligence.

    Recent developments in deep neural networks are enabling major improvements in usable machine intelligence across the globe. Computer vision has already reached a milestone now that machines are able to, on their own, learn how to recognize a cat just by being given lots of images—without needing to know the distinguishing features of a cat.