Categories
Featured

Star Wars should learn from Andor and stop making Disney Plus shows that are so obsessed with the Jedi

[ad_1]

I very much enjoyed the Obi-Wan Kenobi, and stuck it out with Star Wars: Ahsoka, when this highly anticipated Star Wars TV duo landed on Disney Plus. But, by the time the credits had rolled on the latter in mid-2023, I think I’ve had my fill of Jedi-led stories in Lucasfilm’s iconic galaxy far, far away. 

Sure, seeing these series’ Force-wielding protagonists clash with their Sith counterparts and other overtly villainous folks – amid the crackle and buzz of lightsabers – is always highly enjoyable. But the bits in between – channelling the Force and so on – have become rather stale in my eyes. Blah blah “concentrate”, blah blah “use your feelings”… you get the idea.

Even in Disney Plus shows that initially don’t revolve around the Jedi, such as The Mandalorian and The Book of Boba Fett, still tread old ground and eventually lead to the telekinetic hot-glow-stick wielders showing their faces. While I was entertained by the Kill Bill-style battle between Ahsoka and Morgan Elsbeth at the end of The Mandalorian, other appearances from the Jedi have either been a tad underwhelming or overbaked. Yes, I know ‘Baby Yoda’ is cute and all, but seeing Grogu train with a digitally recreated Luke Skywalker in season 2 of The Mandalorian (one of the best Disney Plus shows, in many people’s eyes) wasn’t the dose of nostalgia and role-reversal I think Disney hoped it would be. 

A screenshot of Ewan McGregor as Obi-Wan Kenobi in the Jedi's Disney Plus TV show

Obi-Wan Kenobi’s self-titled Disney Plus series wasn’t as good as it could’ve been. (Image credit: Lucasfilm/Disney Plus)

Andor, though, showed me and many other Star Wars fans that you can make a great Star Wars show without a single Jedi appearance. In fact, I’d argue that Andor is the most interesting piece of Star Wars content that Disney has done to date – something that TechRadar’s senior entertainment reporter Tom Power also claimed in his Andor season 1 review

[ad_2]

Source Article Link

Categories
Life Style

What science can learn from Swiss apprenticeships

[ad_1]

Person standing beneath large equipment at the European Organisation for Nuclear Research

The Compact Muon Solenoid (CMS) detector enabled the discovery of the Higgs boson at CERN, Europe’s particle-physics lab near Geneva.Credit: Richard Juilliart/AFP via Getty

Roughly 100 metres underground, in a tunnel that crosses the border between Switzerland and France, lies the largest machine ever built. The Large Hadron Collider (LHC) compresses and collides tiny bits of matter to recreate the fundamental particles that appeared just one-trillionth of a second after the Universe was created.

It’s all part of a day’s work at CERN, Europe’s particle-physics laboratory near Geneva, which is home to the LHC. The lab, which celebrates its 70th anniversary this year, continues to attract scientists who are eager to uncover the nature of particles that comprise matter. Along with more than 2,600 staff members and 900 fellows, CERN hosted nearly 12,000 visiting scientists from 82 countries in 2022. According to indexed papers on the Web of Science database, the researchers publish, on average, around 1,000 papers each year that explore the origin of the Universe, antimatter, dark matter, supersymmetry and beyond. And their ranks include eminent scientists such as Tim Berners-Lee, credited with inventing the World Wide Web, and physicist Peter Higgs, who died on 8 April.

“Particle physics is basically exploring back in time,” says Alain Blondel, a particle physicist who has worked at CERN and the University of Geneva in Switzerland. “The science we do, together with cosmology, astrophysics and many other fields, explores how the Universe was born and how it works. These are questions that have fascinated people for generations”.

Discoveries made at CERN, such as the production of antihydrogen and the development of the World Wide Web, have affected not only the scientific world, but society as a whole. Yet, the inaccessibility of CERN to the majority of the public has led to an almost mythical perception of the organization, says Andri Pol, a photographer based in Switzerland. Pol spent two years capturing the inner workings of CERN for his 2014 book Inside CERN. “You jump into another world and you feel like an alien,” he says. “I don’t know anything about physics, chemistry or mathematics. But you feel the creativity. There’s a lot of energy not only in the machines, but also the people.”

Brain gain

Retaining and attracting scientific talent was a key driving force behind the creation of CERN. During and after the Second World War, many scientists fled Europe to pursue careers in the United States. In the early 1950s, a small group of European scientists put forth a proposal to create a physics laboratory to unite scientists throughout Europe. On 29 September 1954, 12 member states signed a convention establishing CERN near Geneva (see ‘CERN’S growth’).

A map showing the European countries that formed CERN in the 1950s, In the decades since other nations have joined the alliance.

Source: CERN

Part of the decision to build CERN in Switzerland was the country’s central location in Europe and its neutrality during the war. In fact, CERN’s convention states, “The Organization shall have no concern with work for military requirements.”

“CERN has this aspect of science for peace,” says Rainer Wallny, a physicist at the Swiss Federal Institute of Technology (ETH) Zurich who chaired the Swiss Institute of Particle Physics in 2020–21. “You are not doing anything military related; you work for the curiosity.”

Now, CERN is governed by a council of 23 member states that provide financial contributions and make decisions regarding the organization’s activities, budget and programmes. CERN’s projected annual revenue for 2023 was 1.39 billion Swiss francs (US$1.53 billion), all of which it spends.“I think it is a great model for international collaboration,” says Wallny. “It has a lot of facilities available that are beyond the scope of individual user groups. No one has a particle accelerator in their backyard.”

The LHC, which is the most powerful particle accelerator in the world, consists of a 27-kilometre ring of superconducting magnets. Inside, two particle beams shoot trillions of protons towards one another at nearly the speed of light, causing some to collide and transform their energy into new particles. Along with the LHC, CERN has eight other particle accelerators, two decelerators, an antimatter factory and a vast array of engineering and computing infrastructure.

These resources bring together thousands of scientists from around the world to tackle big questions in particle physics. Research efforts at CERN led to the discovery of weak neutral currents in 1973, the W and Z bosons in 1983 and three types of neutrino in 198913. These findings provided support for the standard model of physics, a theory developed in the 1970s that describes the fundamental particles of the Universe and the four forces that shape their interactions. Then, in July 2012, scientists at CERN found evidence for the last key force in the standard model — the Higgs boson4.

“I’m fascinated by the concept of having these large, international collaborations working on a scientific puzzle,” says Lea Caminada, a particle physicist at the University of Zurich and the Paul Scherrer Institute in Villigen, Switzerland. Caminada and her research group develop pixel detectors for the Compact Muon Solenoid (CMS), a particle detector experiment at the LHC that does research on the standard model, dark matter and extra dimensions. “Doing high-energy physics is unique. It’s really the energy frontier, and there is no other facility in the world where you can do this,” she says.

The CMS collaboration involves more than 5,900 physicists, engineers, technicians and students from 259 institutions across 60 countries. The collaboration publishes around 100 papers each year and celebrated its 1,000th publication in November 2020. But organizing and contributing to large-scale projects is no simple feat. “It’s not always easy to work at CERN. It’s very hard to organize experiments this big,” Caminada says. For instance, she explains, everyone involved in the CMS experiment can review manuscript drafts and provide feedback before submission of a paper. “But I think it creates opportunities for people in different countries.”

A fount of knowledge

Thea Klæboe Åarrestad’s first experience at CERN was during an undergraduate internship in July 2012. During the paid programme, she took three weeks of classes, met with fellow physicists and attended lectures from specialists in the field. It also happened to be the year CERN announced the discovery of the Higgs boson. “Peter Higgs was there. The press of the free world was there. People were sleeping in lines outside the main auditorium to catch the speech,” she says.

Black & white photo of several people standing around large chamber

The Gargamelle chamber at CERN, operational during the 1970s, detected neutrinos.Credit: CERN PhotoLab

Åarrestad went on to earn her PhD from the University of Zurich in 2019, where she worked on the CMS experiment at CERN, and then became a research fellow at CERN from 2019 to 2021. “My daughter was five months old when I started commuting to CERN. I spent eight hours on the train every day,” she says. “My friends questioned whether I could do exactly the same work for a company, and I can honestly say no, I can’t.”

Now, as a particle physicist at ETH Zurich, Åarrestad studies how to use machine learning to improve data collection and analysis methods at CERN. “The environment there is fantastic. You go for a coffee and everyone has ideas and thoughts to discuss. I was always very passionate about physics, and being at CERN just made me even more passionate about it because I shared it with so many others,” she says.

Reverberating impacts

The impact of CERN goes well beyond the smashing together of tiny particles. “Such a vibrant intellectual node radiates out to the universities,” says Wallny. He often sends his graduate students to CERN, where they can gain experience in a large, international setting. “There’s a lot of education happening, and not just in science and engineering. You interact with people from other cultures and learn how to express yourself in English,” he adds.

According to Wallny, lessons from organizing large-scale collaborations at CERN can also be applied to other areas of science, such as quantum computing. “In these large experiments, you have to invent your own governance. You have a bunch of usually quite anarchistic academics who still have to play by some rules. You have to give yourself a constitution and a collaboration board. These approaches can easily be copied in other emerging fields of science,” he says.

Investing in projects such as CERN has benefits for society that expand beyond the bounds of academia. Massimo Florio, an economist at the University of Milan in Italy, calculates the costs and benefits of large-scale research infrastructure projects. In 2018, Florio and his colleagues evaluated how procurement orders from CERN for the production of the LHC affected knowledge production, patent filings, sales and profits for more than 350 supplier companies5.

“There is clear evidence that after they got an order from CERN, even 10 years later, it was transformative for them,” says Florio. “Even if you give zero value to the discovery of the Higgs boson, the knowledge generated along the way has immediate benefits to society.”

Over the past 70 years, technologies developed at CERN to tackle technical and computing challenges have been applied throughout the world. Perhaps the most notable is the World Wide Web, which was developed by computer scientist Tim Berners-Lee in 1989 to rapidly share knowledge among scientists. In medicine, the technologies from particle accelerators and detectors are used in positron emission tomography scanners and radiation methods for cancer treatments, such as hadron therapy6.

Satisfying curiosity

As CERN embarks on its eighth decade of research, the organization is planning to upgrade its accelerators to add to knowledge about the fundamental particles that make up the Universe. Towards the end of 2025, the LHC will be shut down and upgraded to a high-luminosity LHC over about four years. The upgrades aim to increase the machine’s luminosity tenfold, which would result in a larger number of collisions, allowing scientists to observe new events and rare events, such as those producing a Higgs boson, in more detail. “If we’re ever going to produce new physics, we need a lot of data. And in order to get a lot of data, we need more collisions,” says Åarrestad. She notes that upgrades to the LHC will result in almost quadruple the number of collisions that occur now.

Feasibility studies are also being conducted for the potential development of the Future Collision Collider (FCC), a massive, 91-kilometre particle accelerator7. A later phase of the proposed FCC is a hadron collider that could have roughly seven times the collision energy of the LHC. But there are concerns about the costs and environmental impacts of the FCC proposals8, as well as particle-physics research more broadly. “There are a lot of humans that would benefit from that money. It costs energy and affects the environment to do fundamental physics,” says Åarrestad. “But I think it is something we should continue in the future despite the cost and the energy consumption, because in the end, as humans, what are we if we’re not curious about where we’re from?”

Furthermore, says Pol, basic research often leads to real-world advances. “Sometimes, something new comes out of basic, theoretical research — one never knows. So, you have to give people who are really skilled a chance to try and find out what makes us what we are,” he says.

That sentiment holds for non-scientists, as well. While working on a contribution to the 2023 book Collisions: Stories from the Science of CERN, Lucy Caldwell, a novelist and playwright based in Ireland, had the opportunity to visit the organization. There, she met several scientists and published a fictional piece on the basis of her experiences. “As humankind, we tend to tell the same stories over and over in different variations,” she says. “Being able to go somewhere like CERN and talk to the scientists right at the cutting edge of knowledge gives you, as a writer, new images, new words and new concepts. It gives you ways to make old stories fresh again and ways to tell new stories. And I think that’s important for all of us.”

[ad_2]

Source Article Link

Categories
Featured

More security flaws found in popular AI chatbots — and they could mean hackers can learn all your secrets

[ad_1]

If a hacker can monitor the internet traffic between their target and the target’s cloud-based AI assistant, they could easily pick up on the conversation. And if that conversation contained sensitive information – that information would end up in the attackers’ hands, as well.

This is according to a new analysis from researchers at the Offensive AI Research Lab from Ben-Gurion University in Israel, who found a way to deploy side channel attacks on targets using all Large Language Model (LLM) assistants, save for Google Gemini. 

[ad_2]

Source Article Link

Categories
Life Style

Bumblebees socially learn behaviour too complex to innovate alone

[ad_1]

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.

[ad_2]

Source Article Link

Categories
News

Learn how to use CapCut video editing with these fantastic tips and tricks

Learn how to use CapCut video editing app

In the fast-paced world of video content creation, editors are constantly on the lookout for tools that can elevate their work to new heights. CapCut is one such tool that offers a plethora of features designed to enhance the quality and impact of your videos. Whether you’re working on a Windows, Mac, iPhone, or Android device, mastering CapCut can give your projects a professional edge. Let’s delve into some key strategies that can help you make the most of what CapCut has to offer.

One of the standout features of CapCut is its advanced layering and auto cutout tools. These allow you to create visually striking text effects by placing text behind subjects in your videos. This technique adds depth and interest, drawing the viewer’s eye and making your content more engaging. The auto cutout feature is particularly useful, as it helps you isolate the subject from the background, allowing you to insert text seamlessly for a more polished look.

How to use CapCut video editing app

The ability to quickly and effectively edit footage is crucial for maintaining a competitive edge. CapCut, a versatile video editing software, provides a suite of advanced tools that cater to this need. Among its many features, the advanced layering and auto cutout tools stand out for their ability to create dynamic and visually appealing effects.

By utilizing these tools, editors can place text behind subjects within the video, adding a layer of depth that captures the viewer’s attention. The auto cutout feature simplifies the process of separating the subject from the background, enabling a seamless integration of text and imagery that enhances the overall polish of the video.

CapCut’s innovative capabilities extend to the creation of custom AI avatars, offering a novel way to personalize video content. This technology allows users to generate virtual characters, such as an AI news reporter, that can be customized to match the style and tone of the video. The avatars can be designed to reflect a specific appearance and can be synchronized with a script, resulting in an interactive and engaging viewing experience. This feature is particularly useful for content creators looking to add a unique and creative element to their videos, setting them apart from the competition.

CapCut video editing tips and tricks

  • For those looking to enhance their YouTube or TikTok videos, CapCut’s AI-generated studio backgrounds are a perfect solution. With a variety of scenes to choose from, you can easily find a background that complements your video’s theme. This simple addition can significantly improve the visual appeal of your content, making it more attractive to viewers.
  • Another exciting capability of CapCut is the ability to create custom AI avatars. Imagine having an AI news reporter that you can customize to fit the style and tone of your video. This feature not only adds a unique touch to your content but also engages your audience with a personalized experience. You can design the avatar’s appearance and synchronize it with your script, making your videos more interactive and enjoyable.
  • Privacy concerns are paramount in today’s digital landscape, and CapCut addresses this with its masking and keyframe features. These tools allow you to blur out faces or sensitive information, ensuring that your videos respect privacy and adhere to regulations. Whether you’re working on a personal project or a professional assignment, maintaining privacy is essential, and CapCut provides the means to do so effectively.
  • Karaoke videos are incredibly popular, and CapCut’s vocal removal tool is a standout feature for music enthusiasts. This tool enables you to strip the vocals from songs, leaving a clean instrumental track for your audience to sing along to. It’s a simple yet powerful way to engage with viewers and encourage them to participate in your content.

Here are some other articles you may find of interest on the subject of video editing

  • Efficiency is key in video editing, and CapCut’s shortcut keys are designed to speed up your workflow. By learning and using these shortcuts, you can save valuable time and focus on the creative aspects of your projects. This can be especially beneficial when working under tight deadlines or juggling multiple tasks.
  • Collaboration is often a crucial part of the video editing process, and CapCut makes it easier with its timestamped comments feature. This allows you to share your work with others and receive specific, targeted feedback. It streamlines communication with team members or clients, making the revision process more precise and efficient.
  • For those looking to maintain a consistent brand image across their content, CapCut’s brand kit feature is invaluable. It enables you to set a uniform style for all your media assets, ensuring that every piece of content you produce is aligned with your brand’s identity. This consistency helps build recognition and trust with your audience.
  • Editing high-resolution footage can be demanding on your system, but CapCut’s proxy media function offers a solution. It allows you to edit with lower-resolution files, ensuring a smoother workflow. Once you’re ready for final production, you can switch back to the full-resolution clips for the best quality output.
  • Lastly, accessibility is an important consideration for video content, and CapCut’s auto-captioning feature helps make your videos more inclusive. With customizable text templates, you can add clear, legible captions to your videos, improving the viewing experience for a wider audience.

Enhancing Video Content with Advanced AI Editing Tools

For content creators aiming to enhance the visual quality of their videos, CapCut’s AI-generated studio backgrounds offer a range of scenes that can be matched to the video’s theme. This simple addition can significantly elevate the aesthetic appeal of the content, making it more attractive to viewers on platforms like YouTube and TikTok. In addition to visual enhancements, CapCut addresses the critical issue of privacy with its masking and keyframe features. These tools enable editors to blur out faces or sensitive information, ensuring that videos comply with privacy regulations and respect individual privacy, which is essential in today’s digital environment.

Efficiency and collaboration are also key aspects of video editing. CapCut’s shortcut keys and timestamped comments feature facilitate a faster workflow and clearer communication among team members or with clients. The brand kit feature ensures consistency across all media assets, reinforcing a cohesive brand identity. For editing high-resolution footage, the proxy media function allows for a smoother editing process, with the option to revert to full-resolution clips for final production. Lastly, the auto-captioning feature enhances accessibility, making videos more inclusive with customizable text templates for captions.

By integrating these strategies into their editing practices, video creators can fully utilize CapCut’s extensive features to produce content that is not only visually striking but also engaging and accessible to a broad audience. Whether for social media or professional use, these techniques enable creators to work more efficiently and deliver high-quality videos that make a memorable impact.

Filed Under: Guides, Top News





Latest timeswonderful Deals

Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, timeswonderful may earn an affiliate commission. Learn about our Disclosure Policy.

Categories
News

Learn how to use PyTorch for Deep Learning applications

Learn how to use PyTorch for Deep Learning apps

Deep learning is transforming the way we approach complex problems in various fields, from image recognition to natural language processing. Among the tools available to researchers and developers, PyTorch stands out for its ease of use and efficiency. This article will guide you through the essentials of using PyTorch, a popular open-source platform that facilitates the creation and training of neural networks.

PyTorch is an open-source machine learning library developed by Facebook’s AI Research lab (FAIR). It’s known for its flexibility, ease of use, and as a powerful tool for deep learning research and application development. PyTorch excels in three key areas: ease of use, performance, and flexibility, making it a popular choice among researchers and developers alike.

What is PyTorch?

PyTorch is celebrated for its dynamic computational graph that allows for flexible model architectures, and its speed in processing artificial neural networks. It’s widely used in both academic research and industry applications. To begin with PyTorch, you can install it on your local machine, or you can use Google Colab, which offers the added benefit of free GPU access, speeding up your computations significantly.

How to use PyTorch

At the heart of PyTorch are tensors, which are similar to advanced arrays that you might be familiar with from NumPy, but with the added capability of running on GPUs. Understanding how to work with tensors is crucial, as they are the building blocks of any deep learning model. You’ll need to know how to create, manipulate, and perform operations on tensors to enable the complex calculations required for neural networks.

One of the standout features of PyTorch is its autograd package, which automates the differentiation process in neural networks. This means that you don’t have to manually calculate gradients during the training process, which can be a tedious and error-prone task. Instead, autograd keeps track of all operations on tensors and automatically computes the gradients for you, making the optimization of neural networks much more straightforward.

Here are some other articles you may find of interest on the subject of Deep Learning :

Training a neural network in PyTorch involves defining the model’s architecture, selecting a loss function that measures how well the model is performing, and choosing an optimizer to adjust the model’s parameters based on the gradients computed during training. PyTorch provides tools that simplify these steps, allowing you to focus on building and refining your model to improve its accuracy.

Neural Networks

A common type of neural network used in image recognition tasks is the Convolutional Neural Network (CNN). PyTorch makes it easy to construct CNNs by providing layers specifically designed for this purpose, such as convolutional layers and max pooling layers. These layers help process and extract features from input data effectively. Additionally, PyTorch includes functionalities for saving and loading models, which is crucial for deploying your model into production or continuing training at a later time.

Another advantage of PyTorch is its support for GPU acceleration, which can dramatically reduce training times and allow for more complex models. You’ll learn how to leverage this capability to make your training process more efficient, which is especially beneficial when working with large datasets or sophisticated neural networks.

Managing data is a critical aspect of training neural networks, and PyTorch offers convenient tools for this purpose. Its built-in datasets and data loaders help you handle data preprocessing, which is essential for training accurate models. These tools enable you to organize your data, apply necessary transformations, and batch your data for efficient training.

After training your model, it’s important to evaluate its performance to ensure it generalizes well to new, unseen data. PyTorch provides various metrics, such as accuracy, to help you assess your model’s effectiveness. You’ll learn how to use these metrics to evaluate your model and interpret the results, which will help you determine the reliability and robustness of your neural network.

Setting Up Your Environment

  • Installation: Install PyTorch by visiting the official website (pytorch.org) and selecting the installation command that matches your environment. PyTorch supports various operating syhttps://pytorch.org/stems and CUDA versions for GPU acceleration.
  • Development Tools: Consider using Jupyter Notebooks or Google Colab for interactive development. Google Colab also offers free access to GPUs, which can significantly speed up model training.

Working with Tensors

Tensors are the backbone of PyTorch, similar to NumPy arrays but with strong GPU support.

  • Creating Tensors: Use torch.tensor() for manual creation, or utility functions like torch.zeros(), torch.ones(), and torch.rand() for specific types of tensors.
  • Manipulating Tensors: Learn tensor operations such as slicing, reshaping, and concatenating, which are crucial for data preprocessing and model input preparation.
  • GPU Acceleration: Move tensors to GPU by calling .to('cuda') on tensor objects, provided you have a CUDA-enabled GPU.

Autograd: Automatic Differentiation

  • Understanding Autograd: PyTorch’s autograd system automatically calculates gradients—an essential feature for training neural networks. By tracking operations on tensors, PyTorch computes gradients on the fly, simplifying the implementation of backpropagation.
  • Usage: Simply use tensors with requires_grad=True to make PyTorch track operations on them. After computing the forward pass, call .backward() on the loss tensor to compute gradients.

Defining Neural Networks

  • nn.Module: Extend the nn.Module class to define your own neural network architectures. Implement the __init__ method to define layers and forward method to specify the network’s forward pass.
  • Common Layers: Use predefined layers in torch.nn, such as nn.Linear for fully connected layers, nn.Conv2d for convolutional layers, and nn.ReLU for activation functions.

Training Neural Networks

  • Loss Functions: Select a loss function appropriate for your task from torch.nn module, such as nn.CrossEntropyLoss for classification tasks.
  • Optimizers: Choose an optimizer from torch.optim to adjust model parameters based on gradients, like optim.SGD or optim.Adam.
  • Training Loop: Implement the training loop to feed input data to the model, compute the loss, and update model parameters. Utilize DataLoader for batching and shuffling your dataset.

Evaluating and Saving Models

  • Evaluation: After training, evaluate your model on a validation or test set to assess its performance. Use metrics such as accuracy for classification tasks.
  • Saving and Loading: Use torch.save to save your trained model and torch.load to load it. This is crucial for deploying models or continuing training later.

Next Steps

  • Deepen Your Knowledge: Explore PyTorch’s extensive documentation and tutorials to understand advanced concepts and techniques.
  • Community and Resources: Join the PyTorch community on forums and social media to stay updated with the latest developments and share knowledge.

For those who wish to deepen their knowledge of deep learning and PyTorch, there is a wealth of additional resources available. Engaging with comprehensive tutorials and documentation can expand your understanding of both the theoretical aspects and practical applications of training neural networks.

PyTorch is an accessible platform that offers powerful capabilities for those beginning their journey in deep learning. By exploring the features and functionalities outlined in this guide, you’ll gain hands-on experience with PyTorch and lay the foundation for your own deep learning projects. Remember that becoming proficient in deep learning is a continuous process that involves practice and further learning. Keep experimenting and expanding your skills with PyTorch, and you’ll be well on your way to mastering this exciting field.

Filed Under: Guides, Top News





Latest timeswonderful Deals

Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, timeswonderful may earn an affiliate commission. Learn about our Disclosure Policy.

Categories
News

How to Learn a Language with Google Gemini

Google Gemini

This guide is designed to show you how to use Google Gemini to learn a new language. Embarking on the journey of mastering a new language offers a multitude of enriching and rewarding experiences. This endeavor not only serves as a key to unlocking the treasure troves of diverse cultures around the globe but also significantly broadens one’s perspectives, enhancing understanding and appreciation of the world’s rich tapestry of life. Beyond the cultural and social benefits, the process of learning a new language is known to confer notable cognitive advantages, including improved memory, problem-solving skills, and even creativity.

In this digital age, where technology and education intersect to create dynamic learning environments, Google Gemini stands out as a pioneering AI language model specifically designed to revolutionize the way we approach language learning. This cutting-edge tool is engineered to make the language acquisition process not only more efficient but also thoroughly enjoyable. By leveraging advanced artificial intelligence, Google Gemini provides personalized learning experiences, adapting to the individual’s learning pace, style, and preferences. Its interactive exercises, real-time feedback, and immersive language engagement strategies are tailored to significantly enhance retention and comprehension.

In this digital age, where technology and education intersect to create dynamic learning environments, Google Gemini stands out as a pioneering AI language model specifically designed to revolutionize the way we approach language learning. This cutting-edge tool is engineered to make the language acquisition process not only more efficient but also thoroughly enjoyable. By leveraging advanced artificial intelligence, Google Gemini provides personalized learning experiences, adapting to the individual’s learning pace, style, and preferences. Its interactive exercises, real-time feedback, and immersive language engagement strategies are tailored to significantly enhance retention and comprehension.

With Google Gemini, learners are equipped with a powerful ally in their language learning journey, offering a seamless integration of technology and education to optimize learning outcomes. Whether you’re a beginner aiming to lay a solid foundation or an advanced learner striving to polish your fluency, Google Gemini offers a suite of features designed to meet your needs, making it an indispensable resource for anyone looking to expand their linguistic horizons.

Understanding Google Gemini

Google Gemini is a large-scale language model (LLM) developed by Google AI.

LLMs are trained on massive datasets of text and code, allowing them to communicate, generate text, translate languages, and provide assistance like a knowledgeable virtual companion.

With Gemini at your fingertips, you have a wealth of possibilities to boost your understanding, practice, and fluency in your target language.

Strategies for Using Gemini to Learn a Language

Here are some key strategies on how to use Gemini’s powerful abilities to take your language acquisition to the next level:

Personalized Tutoring:

  • Engage in natural conversations with Gemini.
  • Start with basic interactions and slowly increase complexity as your confidence grows.
  • Ask Gemini to explain grammar rules or complex vocabulary in a simple and clear way.

Immersive Translation:

  • Translate words, phrases, or even entire articles to deepen your vocabulary and improve understanding of sentence structures.
  • Have Gemini translate from your target language to your native language to identify patterns and grammatical differences.
  • Take a piece of writing in your native language and ask Gemini to translate it into the language you’re learning. Compare the versions for nuances.

Writing Enhancement:

  • Let Gemini proofread your writing for grammatical errors and awkward phrasing.
  • Ask Gemini to provide alternative ways to express ideas for enhanced writing style.
  • Request prompts on different topics to keep your writing muscles engaged and expand your vocabulary.

Cultural Insights:

Ask Gemini about idioms, proverbs, or slang commonly used in your target language. This adds extra depth to your understanding.

Inquire about the history of the language or cultural events relating to countries where it’s spoken.

Discuss how to properly navigate conversations in different contexts within the culture tied to your target language.

Gamification:

  • Play vocabulary games with Gemini – describe a word and have Gemini guess, or vice versa.
  • Have Gemini tell you a story with specific vocabulary words that you request.
  • Ask conversational riddles in your target language to put your skills to the test.

Tips for Success

  • Consistency is Key: The more you interact with Gemini, the more it learns about your strengths, weaknesses, and preferences.
  • This leads to more tailored support.
  • Combine with Other Resources: While Gemini is an incredible tool, remember to pair it with traditional learning methods like textbooks, lessons, and interactions with native speakers.
  • Stay Motivated: Set realistic goals and milestones to track your progress, and don’t get discouraged by occasional setbacks. Language learning is a marathon, not a sprint.

Conclusion

Integrating Google Gemini into your daily language learning regimen has the potential to transform your educational endeavors dramatically. The platform’s unparalleled capability to produce text that rivals the quality of human output, coupled with its seamless translation features, stands at the forefront of technological advancements in language education. This innovation goes beyond mere translation; it understands context, nuance, and cultural subtleties, making it a robust tool in your arsenal for language mastery.

Moreover, Google Gemini’s personalized response system is a game-changer in tailored education. By adapting to your specific learning style, pace, and preferences, Gemini offers a customized learning experience that is rare in traditional educational settings. This personalized approach ensures that every interaction with the platform is optimized for your educational benefit, making learning not just a task but a journey tailored just for you.

Embracing the capabilities of Google Gemini transforms the journey of language acquisition into an adventure that is faster, more engaging, and enriched with endless fascination. Its technology empowers learners to dive deeper into the intricacies of a new language, encouraging exploration and discovery in ways previously unimaginable. The interactive exercises, immediate feedback, and immersive scenarios presented by Gemini create a learning environment that is not only effective but also incredibly enjoyable. By leveraging the power of Gemini, the process of mastering a new language becomes not just an educational goal, but a vibrant, fun, and infinitely captivating experience.

Here are some more useful Google Gemini Guides:

Image Credit: JESHOOTS.COM

Filed Under: Guides





Latest timeswonderful Deals

Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, timeswonderful may earn an affiliate commission. Learn about our Disclosure Policy.

Categories
News

How to Learn Python with Google Gemini

Learn Python with Google Gemini

This guide is designed to show you how to learn Python with the help of Google Gemini. Python, recognized for its significant popularity within the programming community, is lauded for its exceptional readability, its adaptability across a myriad of applications, and its approachability for individuals taking their initial steps into the world of coding. The emergence of potent language models, such as Google’s Gemini Ultra, in the technological landscape has infused the educational journey of Python learners with a fresh and exhilarating perspective. This evolution presents an invaluable opportunity for novices and experienced programmers alike to tap into Gemini Ultra’s advanced functionalities. By integrating these capabilities into their learning and development process, burgeoning Python programmers can unlock new levels of efficiency, enhance their problem-solving skills, and explore innovative programming paradigms. Herein lies an exploration of strategies for these aspiring developers to effectively utilize Gemini Ultra’s features to enrich their programming expertise and navigate the complexities of Python with greater confidence and creativity.

Understanding Google Gemini

Gemini is a suite of advanced large language models (LLMs) developed by Google AI. These models have a deep understanding of language, code, and various factual topics. Here’s how they can enrich your Python learning experience:

  • Code Explanations: Provide Gemini with a Python code snippet and ask for a line-by-line explanation. This gives you instant analysis of complex concepts and how different code elements interact.
  • Example Generation: Don’t know how to approach a task? Ask Gemini to generate sample Python code to solve a specific problem. Analyze the provided code to understand common structures and coding patterns.
  • Bug Identification: Having trouble with an error? Give Gemini your code and the error message. It can often spot the problem areas and suggest fixes or ways to troubleshoot.
  • Concept Clarification: If you’re struggling with topics like classes, inheritance, or algorithms, have Gemini explain them in plain English, providing analogies and examples.

Getting Started

  1. Access to Google Gemini: Access is currently in an experimental phase. Explore these ways to potentially interact with Gemini:
  2. Install Necessary Tools:

Harnessing Gemini’s Power

Here are some practical scenarios in which Gemini can help during your studies:

  • Targeted Practice: Ask Gemini: “Generate a few Python practice problems on lists and loops.” The given problems let you apply your knowledge and reinforce concepts.
  • Debugging Assistance: Say to Gemini: “I have the following Python code [insert code] and this error message [insert error]. Can you suggest what might be wrong?”
  • Alternative Solutions: Show a working piece of code to Gemini and ask: “Can you provide a different way to write this Python function in a more concise manner?”
  • Documentation Doubts: Ask Gemini: “Explain the parameters of the numpy.array() function in Python.” Gemini often provides clearer or more intuitive explanations than standard documentation.

Important Considerations

  • Gemini is Not a Replacement for Learning: LLMs are incredibly powerful tools, but they don’t substitute for a structured understanding of Python fundamentals. Use Gemini in conjunction with tutorials, courses, and your own experimentation.
  • Output Can Be Inaccurate: LLMs like Gemini are trained on massive amounts of data. However, they can still generate incorrect code or explanations. Always analyze the provided output critically.
  • Evolving Technology: Gemini and its access methods are continuously under development, so expect changes and keep updated on the latest ways to interact with it.

The Future of Learning with AI

Python stands as a widely acclaimed programming language, celebrated for its clear syntax, flexible application across various domains, and its welcoming nature for beginners embarking on their coding journey. The advent of advanced language models, notably Google’s Gemini Ultra, has introduced a thrilling and innovative aspect to the educational journey of aspiring Python enthusiasts. This addition to the programming ecosystem allows newcomers and seasoned developers alike to harness the capabilities of Gemini Ultra to enhance their coding skills, debug code more efficiently, and explore new possibilities in Python programming. Here’s a deeper dive into how emerging Python programmers can leverage the full spectrum of Gemini Ultra’s features to accelerate their learning curve and broaden their coding horizons.

Image Credit: Chris Ried

Here are some helpful Google Gemini guides:

Filed Under: Guides





Latest timeswonderful Deals

Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, timeswonderful may earn an affiliate commission. Learn about our Disclosure Policy.

Categories
News

Learn about generative AI for free with the Alan Turing Institute

Generative AI explained by the Alan Turing Institute

If you would like to learn more about the field of generative artificial intelligence (AI) that is rapidly transforming how we interact with technology. This area of study, which focuses on the creation of new content such as text, images, and audio from existing data, is becoming increasingly relevant in our daily lives. Technologies like ChatGPT and DallE 3 are prime examples of how generative AI can innovate and automate tasks, showcasing its ability to influence our digital experiences.

Generative AI has been around for a while, subtly shaping the way we use technology. Early versions of AI, like Google Translate and Siri, have set the stage for more advanced systems such as GPT-4. These technologies have evolved from simple automated responses to generating complex, human-like text and realistic images, making them more and more a part of our everyday digital interactions.

At its core, generative AI works by mimicking the human brain through language modeling and neural networks. This allows the AI to learn from vast amounts of data on the web, recognizing patterns and associations that enable it to produce content that is both relevant and engaging. However, creating a generative AI model is just the first step. Fine-tuning these models is crucial to ensure that they can perform specific tasks accurately and reliably.

What is generative AI?

One of the most remarkable aspects of generative AI is its ability to improve itself through self-supervised learning. This means that the AI can analyze additional data, identify its own errors, and correct them without human intervention, much like how we learn from our own experiences.

Here are some other articles you may find of interest on the subject of  generative AI

As AI models become larger and more complex, they can produce outputs that are increasingly nuanced and sophisticated. But scaling up these models comes with its own set of challenges, such as managing the computational demands and the potential for errors that can arise.

Generative AI is not without its flaws. Issues such as bias, misinformation, and the generation of irrelevant or nonsensical content—sometimes referred to as “hallucinations”—can lead to distorted outputs that may be unreliable or even harmful. Addressing these challenges is essential for the ethical use of AI.

The impact of generative AI extends beyond the technology itself. There are environmental considerations to take into account, as well as the potential effects on job markets. As AI becomes more prevalent in society, it’s important to ensure that its development aligns with societal values and ethical practices.

Looking ahead, the future of generative AI is likely to involve more efficient system architectures and the need for careful regulation. Despite its progress, AI still faces difficulties in understanding the physical world and human emotions, which highlights the importance of ongoing research and development.

The recent Turing Institute lecture stressed the importance of human involvement in guiding the evolution of AI. As AI continues to advance, it’s crucial to ensure that it serves beneficial purposes, reduces biases, and reflects societal values.

Generative AI is a powerful tool that has the potential to reshape various industries. Understanding its capabilities, limitations, and impact on society is key to harnessing its power responsibly. As we look to the future, it’s clear that generative AI will continue to play a significant role in how we interact with technology, and it’s up to us to steer its development in a direction that benefits everyone.

Filed Under: Technology News, Top News





Latest timeswonderful Deals

Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, timeswonderful may earn an affiliate commission. Learn about our Disclosure Policy.

Categories
News

How to Learn Python Quickly with Google Bard

coding

This guide is designed to show you how to learn Python with the help of AI tools like Google Bard. Python’s popularity is skyrocketing, and for good reason. It’s versatile, beginner-friendly, and opens doors to exciting fields like data science, automation, and web development. But where do you start, especially if you’re eager to learn quickly? Enter Google Bard, your friendly AI companion in this Python adventure.

This guide will equip you with a unique approach to mastering Python, leveraging Bard’s capabilities to boost your learning speed and solidify your understanding. So, grab your laptop, and a curious mind, and let’s dive into the world of Python with Bard by your side!

Step 1: Prime Your Playground

  • Setting Up: Bard prefers you interact through its web interface. However, if you prefer a coding environment, Python interpreters like Thonny or IDLE are great choices for beginners.
  • Bard Basics: Familiarize yourself with Bard’s interface. Learn how to ask clear questions, use code snippets within your queries, and navigate the different response formats.

Step 2: Foundational Footsteps

  • Start with the Fundamentals: Use Bard to grasp basic concepts like variables, data types, operators, and control flow. Ask Bard to explain these concepts using real-world examples and analogies.
  • Interactive Learning: Practice writing simple Python code snippets within your Bard queries. Ask Bard to analyze your code, highlight errors, and suggest improvements.
  • Bite-Sized Lessons: Leverage Bard’s ability to summarize technical content. Ask Bard to condense informative articles, tutorials, or documentation into easily digestible chunks.

Step 3: Dive Deeper with Bard as Your Co-pilot

  • Challenge Yourself: Graduate from simple commands to writing short scripts that solve specific problems. Ask Bard to help you break down the problem into smaller tasks and guide you through the coding process.
  • Practice Makes Perfect: Utilize Bard’s code generation capabilities. Provide a clear description of what you want your code to achieve, and let Bard generate a draft script. Analyze the generated code, understand its logic, and modify it to fit your needs.
  • Get Creative: Don’t just crunch numbers! Explore Python’s versatility. Build simple games, automate tasks, or dabble in web development. Use Bard as your brainstorming partner and ask it to suggest creative Python projects for your skill level.

Step 4: Beyond the Bard: Expanding Your Horizons

  • Structured Learning: While Bard is a powerful tool, don’t neglect the value of structured learning resources. Consider online courses, interactive platforms like Codecademy, or beginner-friendly Python books to solidify your understanding.
  • Community Connection: Immerse yourself in the Python community. Join online forums, attend meetups, or collaborate on projects with fellow Python enthusiasts. Sharing your learnings and challenges can lead to invaluable insights and accelerated growth.

Remember:

  • Consistency is key: Dedicate small, regular chunks of time to practicing Python. Consistent effort, even in short bursts, will yield far greater results than sporadic marathons.
  • Don’t be afraid to ask: Bard is here to help! Don’t hesitate to ask questions, no matter how basic they seem. The more you ask, the more you learn and the faster you progress.
  • Have fun: Learning should be an enjoyable journey. Experiment, explore, and celebrate your victories, big or small. The world of Python is your oyster, so go forth and make the most of it!

With Google Bard as your guide and your own dedication as the engine, you’ll be wielding Python’s power in no time. So, what are you waiting for? Start your Python adventure today and let Bard be your trusted companion on the path to proficiency!

Bonus Resources:

Here is a useful video from Programming with Mosh on how to learn Python:

Summary

Learning Python may seem like a daunting climb, but with Google Bard as your sherpa, the summit is closer than you think. This guide has equipped you with a strategic approach, a supportive companion, and a treasure trove of resources to fuel your journey. Remember, consistent practice, fearless curiosity, and a dash of creativity are your secret weapons. As you conquer each concept, build your confidence, and witness the power of your Python code come alive, the sense of accomplishment will be truly rewarding. So, embrace the challenge, embrace Bard, and embrace the endless possibilities that await you in the vibrant world of Python. Happy coding!
Here are some more useful Google Bard guides:

Image Credit: Kelly Sikkema

Filed Under: Guides





Latest timeswonderful Deals

Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, timeswonderful may earn an affiliate commission. Learn about our Disclosure Policy.