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iPhone 17 might feature “more complex” design, smaller Dynamic Island

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All 2025 iPhone 17 models might have ProMotion, always-on screens
2025 iPhones could pack some big changes.
Photo: Ed Hardy/Cult of Mac

Apple is reportedly planning some big design changes for next year’s iPhone 17 lineup. The changes include a smaller Dynamic Island and a “more complex” aluminum design.

The company might even revamp its lineup with the addition of the iPhone 17 Slim.

2025 iPhones could pack some big upgrades

These claims come from Haitong International Securities analyst Jeff Pu in a note shared with 9to5Mac. He believes Apple will reshuffle its lineup in 2025 and replace the iPhone 17 Plus with a “Slim” model. As the name indicates, the Slim model will seemingly stand out in the lineup with its “slim design.”

The non-Pro variants will include the iPhone 17 with a 6.1-inch display and the iPhone 17 Slim with a 6.6-inch panel. This corroborates a recent rumor suggesting the iPhone 17 Plus might have a smaller display than the iPhone 15 Plus. The Pro lineup will have the 6.3-inch iPhone 17 Pro and the 6.9-inch iPhone 17 Pro Max.

Interestingly, Pu says Apple will debut a “more complex” aluminum design on the iPhone 17, 17 Slim, and 17 Pro in 2025. The iPhone 17 Pro Max will continue using titanium and feature a narrow Dynamic Island. These two features will remain exclusive to the Max model.

The use of “metalens” technology for the proximity sensor will allow the company to reduce the Face ID sensor’s dimensions. In turn, this will reduce the size of the Dynamic Island.

iPhone 17 Pro could sport 12GB RAM

Internally, the analyst claims the iPhone 17 and its Slim sibling will sport an A18 or A19 chip along with 8GB RAM. The Pro variants could sport an A19 Pro chip and 12GB RAM.

All four iPhone 17 models might feature a 24MP front-facing camera. This should allow for better details and sharpness in selfies. It appears Apple will give all the iPhone cameras a resolution bump to enable them to shoot 24MP pictures. Reputed Apple analyst Ming-Chi Kuo also made a similar claim about the iPhone 17’s front-facing camera upgrade earlier this year.

Previous rumors suggest all iPhone 17 variants could sport ProMotion displays with always-on support. If these reports turn out accurate, Apple could have some big changes in store for its iPhone lineup in 2025.



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Bumblebees socially learn behaviour too complex to innovate alone

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Culture in animals can be broadly conceptualized as the sum of a population’s behavioural traditions, which, in turn, are defined as behaviours that are transmitted through social learning and that persist in a population over time4. Although culture was once thought to be exclusive to humans and a key explanation of our own evolutionary success, the existence of non-human cultures that change over time is no longer controversial. Changes in the songs of Savannah sparrows5 and humpback whales6,7,8 have been documented over decades. The sweet-potato-washing behaviour of Japanese macaques has also undergone several distinctive modifications since its inception at the hands of ‘Imo’, a juvenile female, in 19539. Imo’s initial behaviour involved dipping a potato in a freshwater stream and wiping sand off with her spare hand, but within a decade it had evolved to include repeated washing in seawater in between bites rather than in fresh water, potentially to enhance the flavour of the potato. By the 1980s, a range of variations had appeared among macaques, including stealing already-washed potatoes from conspecifics, and digging new pools in secluded areas to wash potatoes without being seen by scroungers9,10,11. Likewise, the ‘wide’, ‘narrow’ and ‘stepped’ designs of pandanus tools, which are fashioned from torn leaves by New Caledonian crows and used to fish grubs from logs, seem to have diverged from a single point of origin12. In this manner, cultural evolution can result in both the accumulation of novel traditions, and the accumulation of modifications to these traditions in turn. However, the limitations of non-human cultural evolution remain a subject of debate.

It is clearly true that humans are a uniquely encultured species. Almost everything we do relies on knowledge or technology that has taken many generations to build. No one human being could possibly manage, within their own lifetime, to split the atom by themselves from scratch. They could not even conceive of doing so without centuries of accumulated scientific knowledge. The existence of this so-called cumulative culture was thought to rely on the ‘ratchet’ concept, whereby traditions are retained in a population with sufficient fidelity to allow improvements to accumulate1,2,3. This was argued to require so-called higher-order forms of social learning, such as imitative copying13 or teaching14, which have, in turn, been argued to be exclusive to humans (although, see a review of imitative copying in animals15 for potential examples). But if we strip the definition of cumulative culture back to its bare bones, for a behavioural tradition to be considered cumulative, it must fulfil a set of core requirements1. In short, a beneficial innovation or modification to a behaviour must be socially transmitted among individuals of a population. This process may then occur repeatedly, leading to sequential improvements or elaborations. According to these criteria, there is evidence that some animals are capable of forming a cumulative culture in certain contexts and circumstances1,16,17. For example, when pairs of pigeons were tasked with making repeated flights home from a novel location, they found more efficient routes more quickly when members of these pairs were progressively swapped out, when compared with pairs of fixed composition or solo individuals16. This was thought to be due to ‘innovations’ made by the new individuals, resulting in incremental improvements in route efficiency. However, the end state of the behaviour in this case could, in theory, have been arrived at by a single individual1. It remains unclear whether modifications can accumulate to the point at which the final behaviour is too complex for any individual to innovate itself, but can still be acquired by that same individual through social learning from a knowledgeable conspecific. This threshold, often including the stipulation that re-innovation must be impossible within an individual’s own lifetime, is argued by some to represent a fundamental difference between human and non-human cognition3,13,18.

Bumblebees (Bombus terrestris) are social insects that have been shown to be capable of acquiring complex, non-natural behaviours through social learning in a laboratory setting, such as string-pulling19 and ball-rolling to gain rewards20. In the latter case, they were even able to improve on the behaviour of their original demonstrator. More recently, when challenged with a two-option puzzle-box task and a paradigm allowing learning to diffuse across a population (a gold standard of cultural transmission experiments21, as used previously in wild great tits22), bumblebees were found to acquire and maintain arbitrary variants of this behaviour from trained demonstrators23. However, these previous investigations involved the acquisition of a behaviour that each bee could also have innovated independently. Indeed, some naive individuals were able to open the puzzle box, pull strings and roll balls without demonstrators19,20,23. Thus, to determine whether bumblebees could acquire a behaviour through social learning that they could not innovate independently, we developed a novel two-step puzzle box (Fig. 1a). This design was informed by a lockbox task that was developed to assess problem solving in Goffin’s cockatoos24. Here, cockatoos were challenged to open a box that was sealed with five inter-connected ‘locks’ that had to be opened sequentially, with no reward for opening any but the final lock. Our hypothesis was that this degree of temporal and spatial separation between performing the first step of the behaviour and the reward would make it very difficult, if not impossible, for a naive bumblebee to form a lasting association between this necessary initial action and the final reward. Even if a bee opened the two-step box independently through repeated, non-directed probing, as observed with our previous box23, if no association formed between the combination of the two pushing behaviours and the reward, this behaviour would be unlikely to be incorporated into an individual’s repertoire. If, however, a bee was able to learn this multi-step box-opening behaviour when exposed to a skilled demonstrator, this would suggest that bumblebees can acquire behaviours socially that lie beyond their capacity for individual innovation.

Fig. 1: Two-step puzzle-box design and experimental set-up.
figure 1

a, Puzzle-box design. Box bases were 3D-printed to ensure consistency. The reward (50% w/w sucrose solution, placed on a yellow target) was inaccessible unless the red tab was pushed, rotating the lid anti-clockwise around a central axis, and the red tab could not move unless the blue tab was first pushed out of its path. See Supplementary Information for a full description of the box design elements. b, Experimental set-up. The flight arena was connected to the nest box with an acrylic tunnel, and flaps cut into the side allowed the removal and replacement of puzzle boxes during the experiment. The sides were lined with bristles to prevent bees escaping. c, Alternative action patterns for opening the box. The staggered-pushing technique is characterized by two distinct pushes (1, blue arrow and 2, red arrow), divided by either flying (green arrows) or walking in a loop around the inner side of the red tab (orange arrow). The squeezing technique is characterized by a single, unbroken movement, starting at the point at which the blue and red tabs meet and pushing through, squeezing between the outer side of the red tab and the outer shield, and making a tight turn to push against the red tab.

The two-step puzzle box (Fig. 1a) relied on the same principles as our previous single-step, two-option puzzle box23. To access a sucrose-solution reward, placed on a yellow target, a blue tab had to first be pushed out of the path of a red tab, which could then be pushed in turn to rotate a clear lid around a central axis. Once rotated far enough, the reward would be exposed beneath the red tab. A sample video of a trained demonstrator opening the two-step box is available (Supplementary Video 1). Our experiments were conducted in a specially constructed flight arena, attached to a colony’s nest box, in which all bees that were not currently undergoing training or testing were confined (Fig. 1b).

In our previous study, several bees successfully learned to open the two-option, single-step box during control population experiments, which were conducted in the absence of a trained demonstrator across 6–12 days23. Thus, to determine whether the two-step box could be opened by individual bees starting from scratch, we sought to conduct a similar experiment. Two colonies (C1 and C2) took part in these control population experiments for 12 days, and one colony (C3) for 24 days. In brief, on 12 or 24 consecutive days, bees were exposed to open two-step puzzle boxes for 30 min pre-training and then to closed boxes for 3 h (meaning that colonies C1 and 2 were exposed to closed boxes for 36 h total, and colony C3 for 72 h total). No trained demonstrator was added to any group. On each day, bees foraged willingly during the pre-training, but no boxes were opened in either colony during the experiment. Although some bees were observed to probe around the components of the closed boxes with their proboscises, particularly in the early population-experiment sessions, this behaviour generally decreased as the experiment progressed. A single blue tab was opened in full in colony C1, but this behaviour was neither expanded on nor repeated.

Learning to open the two-step box was not trivial for our demonstrators, with the finalized training protocol taking around two days for them to complete (compared with several hours for our previous two-option, single-step box23). Developing a training protocol was also challenging. Bees readily learned to push the rewarded red tab, but not the unrewarded blue tab, which they would not manipulate at all. Instead, they would repeatedly push against the blocked red tab before giving up. This necessitated the addition of a temporary yellow target and reward beneath the blue tab, which, in turn, required the addition of the extended tail section (as seen in Fig. 1a), because during later stages of training this temporary target had to be removed and its absence concealed. This had to be done gradually and in combination with an increased reward on the final target, because bees quickly lost their motivation to open any more boxes otherwise. Frequently, reluctant bees had to be coaxed back to participation by providing them with fully opened lids that they did not need to push at all. In short, bees seemed generally unwilling to perform actions that were not directly linked to a reward, or that were no longer being rewarded. Notably, when opening two-step boxes after learning, demonstrators frequently pushed against the red tab before attempting to push the blue, even though they were able to perform the complete behaviour (and subsequently did so). The combination of having to move away from a visible reward and take a non-direct route, and the lack of any reward in exchange for this behaviour, suggests that two-step box-opening would be very difficult, if not impossible, for a naive bumblebee to discover and learn for itself—in line with the results of the control population experiment.

For the dyad experiments, a pair of bees, including one trained demonstrator and one naive observer, was allowed to forage on three closed puzzle boxes (each filled with 20 μl 50% w/w sucrose solution) for 30–40 sessions, with unrewarded learning tests given to the observer in isolation after 30, 35 and 40 joint sessions. With each session lasting a maximum of 20 min, this meant that observers could be exposed to the boxes and the demonstrator for a total of 800 min, or 13.3 h (markedly less time than the bees in the control population experiments, who had access to the boxes in the absence of a demonstrator for 36 or 72 h total). If an observer passed a learning test, it immediately proceeded to 10 solo foraging sessions in the absence of the demonstrator. The 15 demonstrator and observer combinations used for the dyad experiments are listed in Table 1, and some demonstrators were used for multiple observers. Of the 15 observers, 5 passed the unrewarded learning test, with 3 of these doing so on the first attempt and the remaining 2 on the third. This relatively low number reflected the difficulty of the task, but the fact that any observers acquired two-step box-opening at all confirmed that this behaviour could be socially learned.

Table 1 Combinations of demonstrators and observers, with outcomes

The post-learning solo foraging sessions were designed to further test observers’ acquisition of two-step box-opening. Each session lasted up to 10 min, but 50 μl 50% sucrose solution was placed on the yellow target in each box: as Bombus terrestris foragers have been found to collect 60–150 μl sucrose solution per foraging trip depending on their size, this meant that each bee could reasonably be expected to open two boxes per session25. Although all bees who proceeded to the solo foraging stage repeated two-step box-opening, confirming their status as learners, only two individuals (A-24 and A-6; Table 1) met the criterion to be classified as proficient learners (that is, they opened 10 or more boxes). This was the same threshold applied to learners in our previous work with the single-step two-option box23. However, it should be noted that learners from our present study had comparatively limited post-learning exposure to the boxes (a total of 100 min on one day) compared with those from our previous work. Proficient learners from our single-step puzzle-box experiments typically attained proficiency over several days of foraging, and had access to boxes for 180 min each day for 6–12 days23. Thus, these comparatively low numbers of proficient bees are perhaps unsurprising.

Two different methods of opening the two-step puzzle box were observed among the trained demonstrators during the dyad experiments, and were termed ‘staggered-pushing’ and ‘squeezing’ (Fig. 1c; Supplementary Video 2). This finding essentially transformed the experiment into a ‘two-action’-type design, reminiscent of our previous single-step, two-option puzzle-box task23. Of these techniques, squeezing typically resulted in the blue tab being pushed less far than staggered-pushing did, often only just enough to free the red tab, and the red tab often shifted forward as the bee squeezed between this and the outer shield. Among demonstrators, the squeezing technique was more common, being adopted as the main technique by 6 out of 9 individuals (Table 1). Thus, 10 out of 15 observers were paired with a squeezing demonstrator.

Although not all observers that were paired with squeezing demonstrators learned to open the two-step box (5 out of 10 succeeded), all observers paired with staggered-pushing demonstrators (n = 5) failed to learn two-step box-opening. This discrepancy was not due to the number of demonstrations being received by the observers: there was no difference in the number of boxes opened by squeezing demonstrators compared with staggered-pushing demonstrators when the number of joint sessions was accounted for (unpaired t-test, t = −2.015, P = 0.065, degrees of freedom (df) = 13, 95% confidence interval (CI) = −3.63–0.13; Table 2). This might have been because the squeezing demonstrators often performed their squeezing action several times, looping around the red tab, which lengthened the total duration of the behaviour despite the blue tab being pushed less than during staggered-pushing. Closer investigation of the dyads that involved only squeezing demonstrators revealed that demonstrators paired with observers that failed to learn tended to open fewer boxes, but this difference was not significant. There was also no difference between these dyads and those that included a staggered-pushing demonstrator (one-way ANOVA, F = 2.446, P = 0.129, df = 12; Table 2 and Fig. 2a). Together, these findings suggested that demonstrator technique might influence whether the transmission of two-step box-opening was successful. Notably, successful learners also appeared to acquire the specific technique used by their demonstrator: in all cases, this was the squeezing technique. In the solo foraging sessions recorded for successful learners, they also tended to preferentially adopt the squeezing technique (Table 1). The potential effect of certain demonstrators being used for multiple dyads is analysed and discussed in the Supplementary Results (see Supplementary Table 2 and Supplementary Fig. 4).

Table 2 Characteristics of dyad demonstrators and observers
Fig. 2: Demonstrator action patterns affect the acquisition of two-step box-opening by observers.
figure 2

a, Demonstrator opening index. The demonstrator opening index was calculated for each dyad as the total incidence of box-opening by the demonstrator/number of joint foraging sessions. b, Observer following index. Following behaviour was defined as the observer being present on the surface of the box, within a bee’s length of the demonstrator, while the demonstrator performed box-opening. The observer following index was calculated as the total duration of following behaviour/number of joint foraging sessions. Data in a,b were analysed using one-way ANOVA and are presented as box plots. The bounds of the box are drawn from quartile 1 to quartile 3 (showing the interquartile range), the horizontal line within shows the median value and the whiskers extend to the most extreme data point that is no more than 1.5 × the interquartile range from the edge of the box. n = 15 independent experiments (squeezing-pass group, n = 5; squeezing-fail group, n = 5; and staggered-pushing-fail (stagger-fail) group, n = 5). c, Duration of following behaviour over the dyad joint foraging sessions. Following behaviour significantly increased with the number of joint foraging sessions, with the sharpest increase seen in dyads that included a squeezing demonstrator and an observer that successfully acquired two-step box-opening. Data were analysed using Spearman’s rank correlation coefficient tests (two-tailed), and the figures show measures taken from each observer in each group. Data for individual observers are presented in Supplementary Fig. 1.

To determine whether observer behaviour might have differed between those who passed and failed, we investigated the duration of their ‘following’ behaviour, which was a distinctive behaviour that we identified during the joint foraging sessions. Here, an observer followed closely behind the demonstrator as it walked on the surface of the box, often close enough to make contact with the demonstrator’s body with its antennae (Supplementary Video 3). In the case of squeezing demonstrators, which often made several loops around the red tab, a following observer would make these loops also. To ensure we quantified only the most relevant behaviour, we defined following behaviour as ‘instances in which an observer was present on the box surface, within a single bee’s length of the demonstrator, while it performed two-step box-opening’. Thus, following behaviour could be recorded only after the demonstrator began to push the blue tab, and before it accessed the reward. This was quantified for each joint foraging session for the dyad experiments (Supplementary Table 1). There was no significant correlation between the demonstrator opening index and the observer following index (Spearman’s rank correlation coefficient, rs = 0.173, df = 13, P = 0.537; Supplementary Fig. 2), suggesting that increases in following behaviour were not due simply to there being more demonstrations of two-step box-opening available to the observer.

There was no statistically significant difference in the following index between dyads with squeezing and dyads with staggered-pushing demonstrators; between dyads in which observers passed and those in which they failed; or when both demonstrator preference and learning outcome were accounted for (Table 2). This might have been due to the limited sample size. However, the following index tended to be higher in dyads in which the observer successfully acquired two-step box-opening than in those in which the observer failed (34.82 versus 16.26, respectively; Table 2) and in dyads with squeezing demonstrators compared with staggered-pushing demonstrators (25.78 versus 15.76, respectively; Table 2). When both factors were accounted for, following behaviour was most frequent in dyads with a squeezing demonstrator and an observer that successfully acquired two-step box-opening (34.82 versus 16.75 (‘squeezing-fail’ group) versus 15.76 (‘staggered-pushing-fail’ group); Table 2).

There was, however, a strong positive correlation between the duration of following behaviour and the number of joint foraging sessions, which equated to time spent foraging alongside the demonstrator. This association was present in dyads from all three groups but was strongest in the squeezing-pass group (Spearman’s rank order correlation coefficient, rs = 0.408, df = 168, P < 0.001; Fig. 2c). This suggests, in general, either that the latency between the start of the demonstration and the observer following behaviour decreased over time, or that observers continued to follow for longer once arriving. However, the observers from the squeezing-pass group tended to follow for longer than any other group, and the duration of their following increased more rapidly. This indicates that following a conspecific demonstrator as it performed two-step box-opening (and, specifically, through squeezing) was important to the acquisition of this behaviour by an observer.

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How to use Excel Copilot AI assistant to simplify complex tasks

How to use Excel Copilot AI assistant to simplify complex tasks

Microsoft has recently introduced a new feature in Excel that is set to change the way we interact with spreadsheets. This feature, known as Copilot, is an artificial intelligence (AI) tool and assistant that integrates seamlessly into Excel, offering users a more intuitive and efficient way to manage their data. As technology continues to advance, tools like Copilot are indicative of the direction in which software solutions are heading, aiming to make complex tasks more accessible to a broader audience.

To take advantage of Copilot’s full potential, users must have a Microsoft 365 subscription. The Copilot Pro subscription, in particular, unlocks a suite of advanced features designed to enhance productivity. It’s important to note that Copilot is not a standalone application; it works best when files are stored in OneDrive or SharePoint. Additionally, users must format their data into tables before Copilot’s algorithms can be applied effectively.

Microsoft 365

Copilot is integrated into Microsoft 365 in two ways. It works alongside you, embedded in the Microsoft 365 apps you use every day—Word, Excel, PowerPoint, Outlook, Teams, and more—to unleash creativity, unlock productivity, and uplevel skills. Business Chat works across the LLM, the Microsoft 365 apps, and your data—your calendar, emails, chats, documents, meetings, and contacts—to do things you’ve never been able to do before. You can give it natural language prompts like “tell my team how we updated the product strategy” and it will generate a status update based on the morning’s meetings, emails, and chat threads.

Copilot in Excel

Imagine being able to interact with your data by simply asking for what you need in plain language. Copilot can handle a variety of requests, from calculating unique customer counts to adding profit columns, and from creating pivot tables to generating charts. This AI-driven approach is especially beneficial for those who may not be experts in Excel, as it simplifies the use of the program’s sophisticated features. Here are refuted example prompts you can try in Copilot Excel :

  • Give a breakdown of the sales by type and channel. Insert a table.
  • Project the impact of [a variable change] and generate a chart to help visualize.
  • Model how a change to the growth rate for [variable] would impact my gross margin.

Copilot in Excel works alongside you to help analyze and explore your data. Ask Copilot questions about your data set in natural language, not just formulas. It will reveal correlations, propose what-if scenarios, and suggest new formulas based on your questions – generating models based on the questions that help you explore your data without modifying it. Identify trends, create powerful visualizations, or ask for recommendations to drive different outcomes.

Here are some other articles you may find of interest on the subject of Microsoft Copilot AI assistant :

Getting Started with Excel Copilot

1. Requirements:

  • A Microsoft 365 subscription, either a family or a personal plan.
  • An additional subscription to Copilot Pro, granting access to Copilot features across various Microsoft applications including Excel, Word, PowerPoint, and Outlook, along with benefits like using GPT-4 during peak hours and faster image creation with DALL·E 3.

2. Activation:

  • Navigate to the designated website, accessible via a link provided in the video description or a card in the video’s top right-hand corner.
  • Ensure your files are stored in OneDrive or SharePoint as Copilot functions exclusively with cloud-stored files.

Core Features of Excel Copilot

1. Data Analysis and Insights:

  • Formulas and Calculations: Automatically figure out and apply complex formulas based on natural language prompts, significantly reducing the manual effort required in formula creation.
  • Data Visualization: Generate charts and graphs to visually represent data, facilitating easier interpretation and presentation.
  • Highlighting and Sorting: Highlight cells based on specific criteria and sort/filter data seamlessly, enhancing data readability and organization.

2. Efficiency and Productivity Enhancements:

  • Column Addition: Effortlessly add new columns for calculated data, such as profit margins, by simply prompting Copilot with your requirements.
  • Data Conversion: Convert data ranges into tables with a single click, leveraging the advantages of Excel tables like banded rows, quick formatting, and easy data manipulation.
  • Learning and Suggestions: Receive prompt suggestions and sample queries to better engage with Copilot, making the tool accessible to new users and providing inspiration for complex data manipulation tasks.

Practical Applications

1. Simplifying Complex Tasks:

  • Excel Copilot can intuitively understand and execute complex data queries, such as identifying the number of unique customers or calculating total sales per customer, using natural language prompts. This significantly lowers the barrier for performing sophisticated data analysis.

2. Enhancing Data Presentation:

  • The ability to quickly generate and customize charts based on specific data points or trends allows users to present their data in a more impactful manner. Although some customizations may require manual adjustments, Copilot significantly accelerates the initial creation process.

3. Streamlining Data Management:

  • By automating the process of highlighting significant data points, such as high-value transactions, and performing conditional formatting, Copilot aids in quickly identifying key insights within large datasets.

4. Facilitating Advanced Analysis:

  • Copilot can handle requests to analyze data for seasonal trends or outliers, enabling users to identify patterns that may not be immediately apparent through traditional analysis methods.

Limitations and Considerations

While Excel Copilot heralds a new era of data interaction within Excel, it’s important to recognize its current limitations, such as the inability to perform certain customizations directly through AI prompts. Currently, it cannot change chart colors directly, and some power users might notice that its response times are slower than performing tasks manually. Additionally, the tool’s effectiveness is pendant upon clear and precise user prompts, and there may be a learning curve in formulating queries that yield desired outcomes. Despite these initial challenges, the future of Copilot looks promising. It is expected to continue improving and become an indispensable tool for Excel users of all skill levels.

Microsoft Copilot represents more than just a new feature in Excel; it is a step towards making data analysis more democratic and less daunting for users who may not have extensive experience with spreadsheets. As we continue to embrace technological advancements, Copilot is poised to play a significant role in reshaping our interactions with Excel and data management as a whole.

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Training AI to use System 2 thinking to tackle more complex tasks

Training AI LLM to use system 2 thinking to tackle more complex tasks

Artificial intelligence seems to be on the brink of another significant transformation nearly every week at the moment, and this week is no exception. As developers, businesses and researchers  dive deeper into the capabilities of large language models (LLMs) like GPT-4, we’re beginning to see a shift in how these systems tackle complex problems. The human brain operates using two distinct modes of thought, as outlined by Daniel Kahneman in his seminal work, “Thinking, Fast and Slow.” The first, System 1, is quick and intuitive, while System 2 is slower, more deliberate, and logical. Until now, AI has largely mirrored our instinctive System 1 thinking, but that’s changing.

In practical terms, System 2 thinking is what you use when you need to think deeply or critically about something. It’s the kind of thinking that requires you to stop and focus, rather than react on instinct or intuition. For example, when you’re learning a new skill, like playing a musical instrument or speaking a foreign language, you’re primarily using System 2 thinking.

Over time, as you become more proficient, some aspects of these skills may become more automatic and shift to System 1 processing. Understanding the distinction between these two systems is crucial in various fields, including decision-making, behavioral economics, and education, as it helps explain why people make certain choices and how they can be influenced or trained to make better ones.

AI System 2 thinking

Researchers are now striving to imbue AI with System 2 thinking to enable deeper reasoning and more reliable outcomes. The current generation of LLMs can sometimes produce answers that seem correct on the surface but lack a solid foundation of analysis. To address this, new methods are being developed. One such technique is prompt engineering, which nudges LLMs to unpack their thought process step by step. This is evident in the “Chain of Thought” prompting approach. Even more advanced strategies, like “Self-Consistency with Chain of Thought” (SCCT) and “Tree of Thought” (ToT), are being explored to sharpen the logical prowess of these AI models.

The concept of collaboration is also being examined as a way to enhance the problem-solving abilities of LLMs. By constructing systems where multiple AI agents work in concert, we can create a collective System 2 thinking model. These agents, when working together, have the potential to outperform a solitary AI in solving complex issues. This, however, introduces new challenges, such as ensuring the AI agents can communicate and collaborate effectively without human intervention.

Other articles you may find of interest on the subject of training large language models :

To facilitate the development of these collaborative AI systems, tools like Autogen Studio are emerging. They offer a user-friendly environment for researchers and developers to experiment with AI teamwork. For example, a problem that might have been too challenging for GPT-4 alone could potentially be resolved with the assistance of these communicative agents, leading to solutions that are not only precise but also logically sound.

What will AI be able to accomplish with System 2 thinking?

As we look to the future, we anticipate the arrival of next-generation LLMs, such as the much-anticipated GPT-5. These models are expected to possess even more advanced reasoning skills and a deeper integration of System 2 thinking. Such progress is likely to significantly improve AI’s performance in scenarios that require complex problem-solving.

The concept of System 2 thinking, as applied to AI and large language models (LLMs), involves the development of AI systems that can engage in more deliberate, logical, and reasoned processing, akin to human System 2 thinking. This advancement would represent a significant leap in AI capabilities, moving beyond quick, pattern-based responses to more thoughtful, analytical problem-solving. Here’s what such an advancement could entail:

  • Enhanced Reasoning and Problem Solving: AI with System 2 capabilities would be better at logical reasoning, understanding complex concepts, and solving problems that require careful thought and consideration. This could include anything from advanced mathematical problem-solving to more nuanced ethical reasoning.
  • Improved Understanding of Context and Nuance: Current LLMs can struggle with understanding context and nuance, especially in complex or ambiguous situations. System 2 thinking would enable AI to better grasp the subtleties of human language and the complexities of real-world scenarios.
  • Reduced Bias and Error: While System 1 thinking is fast, it’s also more prone to biases and errors. By incorporating System 2 thinking, AI systems could potentially reduce these biases, leading to more fair and accurate outcomes.
  • Better Decision Making: In fields like business or medicine, where decisions often have significant consequences, AI with System 2 thinking could analyze vast amounts of data, weigh different options, and suggest decisions based on logical reasoning and evidence.
  • Enhanced Learning and Adaptation: System 2 thinking in AI could lead to improved learning capabilities, allowing AI to not just learn from data, but to understand and apply abstract concepts, principles, and strategies in various situations.
  • More Effective Human-AI Collaboration: With System 2 thinking, AI could better understand and anticipate human needs and behaviors, leading to more effective and intuitive human-AI interactions and collaborations.

It’s important to note that achieving true System 2 thinking in AI is a significant challenge. It requires advancements in AI’s ability to not just process information, but to understand and reason about it in a deeply contextual and nuanced way. This involves not only improvements in algorithmic approaches and computational power but also a better understanding of human cognition and reasoning processes. As of now, AI, including advanced LLMs, primarily operates in a way that’s more akin to human System 1 thinking, relying on pattern recognition and rapid response generation rather than deep, logical reasoning.

The journey toward integrating System 2 thinking into LLMs marks a pivotal moment in the evolution of AI. While there are hurdles to overcome, the research and development efforts in this field are laying the groundwork for more sophisticated and dependable AI solutions. The ongoing dialogue about these methods invites further investigation and debate on the most effective ways to advance System 2 thinking within artificial intelligence.

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Midjourney 6 advanced prompts for creating complex AI images

Midjourney 6 advanced prompts

Thanks to the power of artificial intelligence our ideas can be transformed into stunning visuals with just a few keystrokes. Midjourney version 6, an artificial intelligence AI image generation tool, is reshaping the landscape of digital creativity. This AI is not merely altering the way we generate images; it’s establishing a new benchmark for creative innovation. Artists, designers, and anyone fascinated by the intersection of technology and art will find that Midjourney 6 advanced prompt capabilities open up a new universe of possibilities.

In the realm of fashion design, envision the ease of typing a description and watching a unique fashion design spring to life. Midjourney version 6 makes this possible, allowing for the creation of branded fashion items through simple text instructions. This significant step could transform the fashion industry by providing a quicker, more imaginative approach to designing, visualizing, and marketing new clothing and accessories.

Midjourney 6 advanced prompts

One of the most striking features of Midjourney is its ability to produce images with incredible realism. The AI crafts visuals with such precise textures and details that they can compete with the output of professional photographers and designers. This means that creating portraits, product images, and any visual content that demands a high level of authenticity is now easier than ever before.

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The versatility of Midjourney’s AI is evident in its wide range of creative outputs. Whether it’s manga covers that capture the essence of the genre or product photography that makes physical samples unnecessary, Midjourney is pushing creative boundaries. It even allows individuals to customize their digital spaces with unique wallpapers that showcase their personal style.

Another area where Midjourney excels is in the seamless integration of text and imagery. This is particularly significant for the fields of advertising and editorial content. As the technology continues to develop, it is poised to become a vital tool for communicators, enabling a perfect marriage of visual and textual storytelling.

But the use of Midjourney’s AI-generated art isn’t confined to professional spheres. It can serve as a brainstorming tool, assisting in the visualization of complex ideas. The personalized art pieces it creates can also serve as modern, individualized decor for your living space. The potential of Midjourney’s prompts is continuously being discovered. As users experiment and share their creations, our collective understanding of what the AI can achieve grows. The community’s ongoing exploration provides a wellspring of inspiration for creative endeavors.

Midjourney version 6 is more than just a tool; it’s a portal to a world where your creativity knows no bounds. Whether it’s transforming the fashion industry or adding a personal touch to home decor, the impact of its advanced prompt capabilities is significant. Keep an eye out for further developments that will broaden your creative scope with this sophisticated technology.

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How to Answer Complex Questions with Google Bard

Answer Complex Questions Google Bard

This guild is designed to show you how you can answer complex questions with the help of Google Bard and other AI assistants. Google Bard, a large language model (LLM) developed by Google AI, has quickly gained popularity for its ability to provide comprehensive and insightful answers to a wide range of questions. While it excels at answering simple and straightforward questions, its true potential lies in its capability to tackle more intricate and challenging inquiries. This article delves into the art of effectively employing Google Bard to address complex questions, empowering you to extract maximum value from this remarkable AI tool.

1. Frame Your Question Clearly

The first step towards obtaining a satisfying answer is to articulate your question with clarity and precision. Avoid vague or ambiguous language, as Bard may misinterpret your intent. Instead, structure your question in a way that provides context and guides Bard towards the specific information you seek. For instance, instead of asking “What is the meaning of life?”, rephrase it as “Explore the various philosophical perspectives on the concept of life’s meaning.”

2. Provide Background Information

If the question involves a particular subject or topic, offer Bard some background knowledge to enhance its understanding. This could include key concepts, relevant theories, or historical context. By providing a foundation, you enable Bard to grasp the nuances of the topic and provide a more informed response. For example, if inquiring about the intricacies of quantum mechanics, briefly explain the basic principles and terminology involved.

3. Break Down Complex Questions

Complex questions often encompass multiple sub-questions or require a step-by-step approach. To address such inquiries effectively, divide them into smaller, more manageable chunks. This allows Bard to focus on specific aspects of the question and provide a more detailed and comprehensive response. For instance, if questioning the origins of the universe, break it down into smaller questions like “How did the Big Bang theory emerge?” or “What evidence supports the expansion of the universe?”.

4. Use Specific Keywords

Incorporate relevant keywords into your question to guide Bard toward the desired information. This helps it identify the specific areas of its knowledge base that are most pertinent to your query. For example, if asking about the impact of artificial intelligence on society, include keywords like “social implications”, “employment trends”, or “ethical considerations”.

5. Leverage Multiple Prompts

If a question is particularly intricate or requires extensive research, consider employing multiple prompts to break it down into manageable sections. This approach allows Bard to focus on specific aspects of the question and provide a more thorough analysis. For example, when exploring the history of the Titanic, use prompts like “Discuss the engineering challenges of the Titanic’s construction” or “Analyze the factors that contributed to the ship’s sinking”.

6. Cross-Verify and Contextualize Answers

While Google Bard is remarkably adept at providing informative answers, it’s essential to cross-check its responses with credible sources to ensure accuracy and completeness. This could involve consulting academic papers, expert websites, or reputable news organizations. Furthermore, contextualize Bard’s answers by considering the sources it references and the overall context of the discussion.

7. Utilize Google Search for Enhanced Exploration

Google Bard serves as a valuable tool for initial research and exploration of complex questions. However, if you require more in-depth analysis or additional perspectives, leverage Google Search to delve deeper into specific topics and uncover further information. This combination of Bard’s insights and Google Search’s breadth of knowledge can provide a comprehensive understanding of complex issues.

8. Engage in Active Learning and Exploration

As you interact with Google Bard and explore its capabilities, engage in active learning. Observe how it responds to different types of prompts, identify its strengths and limitations, and experiment with different phrasing and structures to refine your questions. This active engagement will enhance your understanding of Bard’s potential and allow you to effectively utilize it for addressing complex inquiries.

9. Embrace Continuous Improvement

Remember that Google Bard is still under development and continuously evolving. As it learns and expands its knowledge base, its ability to handle complex questions will undoubtedly improve. Stay updated on its latest advancements, utilize its feedback mechanisms to provide suggestions, and participate in its community to contribute to its evolution.

By employing these strategies, you can unlock the full potential of Google Bard to effectively address complex questions, gaining valuable insights and expanding your knowledge across a wide range of topics. As you explore its capabilities, continuously refine your approach, and embrace its ongoing development, Google Bard will become an invaluable resource for your intellectual pursuits.

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