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]


IBM on its '5 in 5' predictions for 2019 Watch Now

Last week's issue of Nature magazine contained some intriguing work by IBM and MIT concerning how to implement machine learning on a quantum computer.

The work suggests aspects of machine learning where quantum could actually have a measurable advantage over classical, meaning, electronic, and computers.

Whether that adds up to a "killer app" for quantum is less certain. It's not enough to do something in quantum computing that's hard to do in classical computing; it has to be something that's worth doing.

Researchers at IBM's T.J. Watson Research Center, including Vojtěch Havlíček, Antonio D. Córcoles, Kristan Temme, Abhinav Kandala, Jerry M. Chow and Jay M. Gambetta, teamed up with Aram W. Harrow of MIT's Center for Theoretical Physics, to author the Nature paper, titled, Supervised learning with quantum-enhanced feature spaces. There is a separate article of supplementary material as well that is definitely worth reading. (Subscription to Nature, or individual article purchase, is required to read the articles.)

Also: IBM hits quantum computing milestone, may see 'Quantum Advantage' in 2020s

There is also a blog post, penned by Temme and Gambetta.

The researchers demonstrated machine learning running on IBM's "IBM Q" quantum machine, and they did it with just two qubits, so it's a practical system that's operable today, without having to wait for the tens of qubits that may come online in the next decade.

IBM.

With these qubits, they built a classifier, a program that learns how to assign data to different categories by picking out patterns in the data. They found that they could compute a more complex function than conventional computers if they built their classifiers using two of what are called "Hadamard gates," a transformation of the data that is akin to a Fourier transform.

The version of machine learning that they pursue in this case is not deep learning, it's what has traditionally been called a "shallow" network, a quantum version of the "support vector machine," or SVM, that was introduced in the 1990s by Vladimir Vapnik.

The SVM has a single "kernel" of weights that transform input data into a "feature map" so that the data can be decisively separated and put into distinct buckets by category of thing. Havlíček and colleagues went looking for a feature map that is hard to compute on a classical computer. They found some, they report, that require the multiple Hadamard gates mentioned above.

The question is whether anyone wants a single, extremely complex feature map. The field of deep learning has for many years now spent a lot of effort to assert the inferiority of the SVM approach, and of similar kernel approaches, in favor of deep neural networks such as convolutional neural networks (CNNs), or recurrent neural networks (RNNs).

Also: Intel offers AI breakthrough in quantum computing

The reason, as explained by the University of Montreal's MILA institute's Yoshua Bengio and colleagues, in 2013, is that deep networks afford hierarchies of representation. The whole point of deep learning is that being constrained by computational limits forces a discipline upon the deep network that makes it produce abstractions that lead to meaningful generalization.

As Bengio writes, "The concepts that are useful for describing the world around us can be defined in terms of other concepts, in a hierarchy, with more abstract concepts higher in the hierarchy, defined in terms of less abstract ones."

Intelligence, in terms of deep learning forms of machine learning, comes out of constraint. Constraint forces levels of abstraction that lead to more sophisticated representations of data. The IBM researchers are also seeking to construct a representation, only in their case, a single, very hard-to-compute feature map.

Must read

That very hard-to-compute feature map will have to contend with a continued fascination with the phenomenon of depth in deep networks. For example, Stanford University's Ben Poole and colleagues have in recent years explored the geometry of what happens to feature vectors as they traverse a deep neural network. They found that the "manifold," the geometric representation of the data, changes shape in intriguing ways as it goes from order to chaos to order again along the length of a deep neural network.

All of which is to say, the current age is fascinated by depth in a deep neural network, and by what happens to signals as they are transformed in a multi-stage process. The field is gripped by the question of why a computer arrives at hierarchies of representation, not just that it can successfully classify.

The IBM classifier might also have some interesting things to say about representations and how they form, though that doesn't seem to be the focus of the current report. Havlíček and colleagues acknowledge that they haven't yet found the quantum advantage they want, "because we minimized the scope of the problem based on our current hardware capabilities, using only two qubits of quantum computing capacity, which can be simulated on a classical computer."

However, they insist a future classifier will "classify far more complex datasets than anything a classical computer could handle," and is hence, "a promising path forward."

In an accompanying piece in Nature, Maria Schuld, with the Quantum Research Group at the School of Chemistry and Physics in the University of KwaZulu-Natal in Durban, South Africa, describes how her research group arrived at a similar discovery to Havlíček and colleagues. In her description of their work, Schuld asks, "Would these techniques be good enough to beat almost 30 years of classical methods?"

If so, it would mean "the desperate search for a 'killer application' for quantum computers would be over," she observes, before adding, "But the answer to this question is probably more complicated."

Will IBM have a quantum advantage in the next decade? Let me know what you think in the comments section.

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