You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Photo by Seth Macey on Unsplash

We are now in the middle of 2018 and Deep Learning research is advancing at exponential rates. At the beginning of this year, I made ten predictions on what to expect for the year. Making predictions and comparing them in hindsight is one way to determine if one’s expectations are overshooting reality. It turns out; it’s undershooting reality in one aspect. I had not expected to see this much new research in “self-awareness.”

It’s a consensus understanding that self-awareness in machines can lead to more autonomous machines and ultimately into machines with consciousness. Previous to this year, the very idea of creating automation with just a minute trace of self-awareness remained a completely abstract notion. Today, however, the idea has morphed into an active research area!

Late last year, I had written that embodied learning was essential to general intelligence. At that time, there weren’t many published research that explored this idea concerning deep learning. On February of this year, Stanford revealed a paper in Arxiv “Emergence of Structured Behaviors from Curiosity-Based Intrinsic Motivation” that began to explore the emergence of what is known as ego-motion. Ego-motion is that self-awareness of an entity that knows its location and direction within a space. The architecture of the Stanford paper is depicted as follows:

(Nick Haber, Damian Mrowca, Li Fei-Fei, Daniel L. K. Yamins)

We shall see that the conceptual model of that of maintaining a ‘world model’ and the Siamese network of comparing the world model and that of the perceived environment is a recurring design pattern in self-aware architectures. The surprising result of the Stanford paper is the progression of capabilities that were learned through a mechanism of curiosity. This chart:

(Nick Haber, Damian Mrowca, Li Fei-Fei, Daniel L. K. Yamins)

demonstrates the progression of capabilities from learning ego-motion, object attention and finally object interaction. This is an impressive development that hasn’t been widely disseminated. The key revelation here is that you can begin with ego-motion and then learn more advanced cognitive capabilities such as object attention and interaction learning. Ego-motion has been empirically verified to be good base that leads to more advanced cognitive capability. In the quest of the holy grail, it’s important to identify the stepping stones. To achieve Conversational Cognition, self-awareness is essential, and you can’t interact with other individuals with first knowing how to interact with objects. Evolution has invented other kinds of brains before constructing the mammalian brain. Expect different architectures for different kinds of capabilities.

Not to be out done, DeepMind submitted to ICLR 2018 in late February a paper titled “Learning Awareness Models” where a system is trained to grasp blocks and predict its interaction with the block. Another related paper (also revealed in February) was Google and DeepMind’s “Machine Theory of Mind.” This paper explores the ability of automation to predict the “mental states of others, including their desires, beliefs, and intentions.”

The subsequent month (i.e. March 2018) Judea Pearl published a paper that explored the “Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution.” Pearl writes:

Our general conclusion is that human-level AI cannot emerge solely from model-blind learning machines; it requires the symbiotic collaboration of data and models.

Where he astutely recognizes the limitation of previous deep learning models. That is, a deep learning system must be aware of the models that it is learning. Cognitive systems that have zero awareness of their latent models of reality are always going to be extremely limited. The architecture element of maintaining a ‘world model’ is absolutely key to more intelligent systems.

Let’s fast forward to this week. DeepMind published a fascinating paper on Science with the simple title “Neural scene representation and rendering.” The groundbreaking capability of DeepMind’s system is the ability to “imagine” 3D scenes from just a few snapshots of the original scene:

S. M. Ali Eslami et al.

Honestly, it’s quite incomprehensible as to how it’s able to create such a high-fidelity 3D world model. This will require a lot of digging since it’s just unclear where this quantum leap of development originates from. What is the prior research work that enables this kind of capability?

The present architecture that is described doesn’t reveal much other than the presence of a Siamese structure and a generative model:

S. M. Ali Eslami et al.

To gain some kind of intuition as for how this might work is to perhaps understand what the two networks are actually doing.

The first network as we saw earlier, is a recurring pattern that is used in ego-motion networks. In 2015, “Learning to See by Moving” for UC Berkeley:

In 2017, Stanford published “Generic 3D Representation via Pose Estimation and Matching”, that forms the basis of this Siamese network architecture. The following architecture:

was designed to learn the 3D representation given two images. The objective was through learning this internal representation that it could generalize to other tasks such as scene layout, object pose estimation and identifying surface normals. If you notice that on the right side, there is a ‘query’ component that appears to be identical to DeepMind’s paper. DeepMind’s paper differs in the use of an additional generative network.

The motivation for this generative model appears to come from an earlier DeepMind paper from 2016: “Towards Conceptual Compression” (note: DeepMind has a habit of favoring unimpressive titles that can be very easily overlooked in one’s research). One of the big research problems about generative models is how do we create them while preserving the underlying semantics. Generative models are very good at rendering realistic images (see: The Uncanny Valley for Deep Learning); however they also generate unrealistic models. This reveals that the underlying representation is unable to capture the semantic relationships between the components that it can generate. How is DeepMind’s network able to capture semantics in an unsupervised manner?

The “Conceptual Compression” (NIPS 2016)paper provides a bit of a hint of how this may be done:

In the paper, they describe what they mean by “conceptual compression” as follows: “giving priority to higher levels of representation and generating the remainder.” If you notice in the diagram above, this architecture is building a kind of representation at each layer. It’s described as follows:

Assume that the network has learned a hierarchy of progressively more abstract representations. Then, to get different levels of compression, we can store only the corresponding number of topmost layers and generate the rest. By solving unsupervised deep learning, the network would order information according to its importance and store it with that priority.

This is a kind of meta-learning process that can create abstraction without even understanding what that abstraction is about. This is indeed extremely surprising. One other approach to this is DeepMind’s β-VAE, which is a way of disentangling representations. This was revealed in April this year. That is, there can be several invariant metrics that can be used as a measure that drives towards introspectable models.

What do they usually say about “standing on the shoulders of giants”? It turns out that two key components of this recent DeepMind development come from way back in 2016.

Here’s the key though, to get to ‘self-awareness’, you need to have a way to create latent models that capture the semantics of the underlying training set. So what design patterns have we just learned here? (1) The use of curiosity for learning new capabilities (2) The importance of explicit models that can be introspected (3) a Siamese network for generating 3D models (4) a method for conceptual compression. Other ingredients still need to be experimentally identified, by we are rapidly getting there!

Many prognosticators are predicting an AI winter. These are likely researchers that have either little exposure in the field or don’t have a good conceptual model to identify key milestones. It is extremely critical to have a good conceptual model of cognition to be able to wade through the thousands of papers that are published every year in Deep Learning. Just this year, 4900 papers were submitted to NIPS:

To give you a visceral idea of what that means. If you read ten papers per day, then it would take you 16 months to read all 4,900 papers. To weed through all the noise, you must know what research to look for. To do this, you need a good conceptual model of human cognition.

Explore Deep Learning: Artificial Intuition: The Improbable Deep Learning Revolution

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