We think it's (Any)time for a bank to make your life easier, not harder and breath new life to the banking system

Anytime

When was the last time you used a chatbot? It’s probably more recent than you think. A 2022 survey from Tidio found that of participants had a minimum of one conversation with a chatbot in the last 12 months, and throughout the world, there are roughly 1.5 billion people using chatbots. 88% However, the time of the preprogrammed chatbots—capable of providing five answers— is over: Generative AI and large language models (LLMs) have arrived. And while LLM chatbots are revolutionizing customer service, providing multilingual support, and analyzing data-driven insights—saving businesses millions of dollars—its uses have now diversified and expanded to education, in particular, language learning. Theoretically, LLM chatbots could be the most innovative tool language learning has ever experienced, as they’re interactive, accessible, and affordable. has even claimed they could teach children to read in 18 months rather than years. After all, using an LLM is a more interactive experience as they’re not deterministic, and their answers cannot be controlled. So it’s their capability of absorbing, processing, and creating new data—specializing in the human language—that makes them especially , particularly language learning. Bill Gates applicable to education But will their erratic hallucinations and inaccuracies, caused by the data sets they’ve been trained on and their inability to understand the prompt information, make them unsuitable for teaching? Let’s dive into what makes an LLM chabot tick, its educational use cases, and why it won’t be vying for language teacher jobs yet. The science behind the chatbots The way that chatbots work varies depending on the chatbot; however, in this article, LLMs—a subset of generative AI, like Google’s Bard and OpenAI’s ChatGPT—will be the main focus. However, it’s right to acknowledge where chatbots in language learning have come from, as other famous language apps, such as Duolingo, also use AI chatbots, particularly machine learning (ML) and natural language processing (NLP) models. Although, the limitations here are that this type of framework is back-coded, meaning it’s programmed to check and only validate correct answers; it’s deterministic. While this type of question-answer language testing is beneficial when acquiring a new language, LLMs can delve much deeper. LLMs specialize in NLP and can generate and mimic human language, making them ideal for language learning. Real-world uses can include machine translation, text summarization, question-answering systems, chatbots, and content generation—they excel at understanding the context and semantics of text data. And due to their expansive uses, LLMs are characterized by their enormous size and computational requirements as they contain billions of parameters. On the other hand, traditional ML generally focuses on understanding data, making predictions, solving specific tasks from pattern recognition, and labeled and unlabelled data. And within language learning, the applications of ML can vary from speech recognition to fraud detection (finding students who like to cheat) and predictive analytics. It’s best used for tasks where data is structured and represented numerically. And unlike LLMs, traditional ML models are smaller and computationally less demanding, depending on the complexity of the task and the chosen algorithm. LLMs: The new super tool for language classes? In a typical language lesson, a teacher could have anywhere from 8 to 20 students with varying levels (and interests) in the language. So, in a lesson where a teacher may need to answer questions from 50% of the class, this spreads the tutor’s time and energy a little thin. Therefore, LLMs are ideal for teaching the basics, like grasping simple grammar rules and practicing vocabulary. With LLM chatbots, users can use school-sanctioned laptops and ask for grammar rules to be explained as if they were five, 10, or 15 years old, catering to every level of student. Or they can ask for a grammar rule to be explained using football terminology, making it personal for individual students and hence, something they’re more likely to pay attention to. This increased engagement means users are likely to return and practice again. Additionally, LLM chatbots are excellent alternatives for conversation practice as they can mimic a native speaker. Plus, their interactive and entertaining nature is another key reason which keeps people interested. And all teachers know that engagement can be one of the biggest hurdles in language learning. One of the significant benefits of LLM chatbots is their widespread accessibility. Anyone can create an account and access applications such as Chat GPT or Google’s Bard, as they both have competent free versions. So, as long as you have a stable internet connection, any student from around the world can leverage the practical capabilities of an LLM chatbot. However, nothing in life, and AI, is perfect. Unraveling the pitfalls of LLMs in language learning While LLMs are valuable tools for language learning, one of the foundational pillars of grasping a new language is fluency, and this skill cannot be achieved through chatting online with an LLM. As chatbots are mainly based on written communication, they could hinder pronunciation and speaking skills if they’re the only tool used in language learning. Only a human teacher can help students perfect their abilities, provide demonstrations, and, more importantly, corrections. One of the most significant issues surrounding the development of generative AI and LLMs is the AI hallucination state. This occurs when chatbots don’t recognize or understand the question or information they’ve been given. Therefore, they go ‘AWOL’ and start fabricating responses meaning they can provide inaccurate information and may reinforce errors or misconceptions. LLMs absorb data from the internet and then generate new data based on these sources—and since anyone can post on the internet—incorrect and prejudicial information can infiltrate LLMs which struggle to differentiate between fact and fiction. These types of hallucinations can undermine trust, and the BBC reported that . So, when used in an educational setting with children, the non-deterministic aspect of LLMs creates a safeguarding issue. Human teachers must go through rigorous checks to work with children: If similar moderation isn’t applied to LLM chatbots, this could place children in a potentially harmful situation. ChatGPT has been known to ask students for sexy pictures Moreover, learning a language doesn’t only consist of mastering conjugations, tenses, and grammar rules. Language learning encompasses much more, such as cultural understanding, idiomatic expressions, and contextual usage. Although an LLM chatbot can provide some idioms or slang on request, it cannot try to rival a knowledgeable teacher, who, for example, can provide the essential nuances between Spanish-speaking countries. Further to this, generally speaking, students must take the initiative when using a chatbot. Users need to know what prompts to ask, and beginners, for example, are unlikely to possess this knowledge. Additionally, the potential of LLM chatbots to aid students in cheating during exams—such as providing answers or speech recognition capabilities—has prompted some Seattle and California school districts to block platforms like ChatGPT. There’s no denying that generative AI, particularly LLM chatbots, is an incredible tool that is revolutionizing many aspects of business and is on a trajectory to be implemented in schools soon. However, while the model’s accessibility and affordability can overcome two major hurdles when learning a new language, its unpredictable inaccuracies and hallucinations mean that the current technology cannot rival and replace a good language teacher. Generative AI has a significant obstacle to overcome regarding its hallucination issue, but; in the right circumstances, LLMs can be helpful tools for supervised homework and grammar practice. Overall, teachers should be safe in the knowledge that, at present, generative AI won’t be taking over the education system.

Why LLM Chatbots Won’t Be Replacing Language Teachers Any Time Soon

কেন এলএলএম চ্যাটবট শীঘ্রই যে কোনও সময় ভাষা শিক্ষকদের প্রতিস্থাপন করবে না

Warum LLM-Chatbots in absehbarer Zeit keine Sprachlehrer ersetzen werden

Por qué los chatbots LLM no reemplazarán a los profesores de idiomas en el corto plazo

Pourquoi les chatbots LLM ne remplaceront pas les professeurs de langues de si tôt

क्यों एलएलएम चैटबॉट जल्द ही भाषा शिक्षकों की जगह नहीं लेंगे

LLM チャットボットがすぐに言語教師に取って代わることがない理由

LLM 챗봇이 곧 언어 교사를 대체하지 않는 이유

Por que os chatbots do LLM não substituirão os professores de idiomas tão cedo

Yüksek Lisans Chatbotları Neden Yakın Zamanda Dil Öğretmenlerinin Yerini Almayacak?

Tại sao Chatbot LLM sẽ không sớm thay thế giáo viên ngôn ngữ

为什么法学硕士聊天机器人不会很快取代语言教师

This is by (source: ). Fischer is a Theoretical quantum physicist. Twitter thread Kevin Fischer @KevinAFischer 05-29-2023 I don't talk much about this - I obtained one of the first FDA approvals in ML + radiology and it informs much of how I think about AI systems and their impact on the world. If you're a pure technologist, you should read the following: There's so much to unpack for both why Geoff was wrong, and why his future predictions should not be taken seriously either. Geoff made a classic error that technologists often make, which is to observe a particular behavior (identifying some subset of radiology scans correctly) against some task (identifying hemorrhage on CT head scans correctly), and then to extrapolate based on that task alone. The reality is that reducing any job, especially a wildly complex job that requires a decade of training, to a handful of tasks is quite absurd. Here's a bunch of stuff you wouldn't know about radiologists unless you built an AI company WITH them instead of opining about their job disappearing from an ivory tower. (1) Radiologists are NOT performing 2d pattern recognition - they have a 3d world model of the brain and its physical dynamics in their head. The motion and behavior of their brain to various traumas informs their prediction of hemorrhage determination. (2) Radiologists have a whole host of grounded models to make determinations, and actually, one of the most important first order determination they make is whether there is anything notably wrong with a brain structure that "feels" off. As a result, classifiers aren’t actually performing the same task even as radiologists. (3) Radiologists, because they have a grounded brain model, only need to see a single example of a rare and obscure condition to both remember it and identify it in the future. This long tail of rare conditions to avoid missing is a large part of their training, and no one has any clue how to make a model that acts similar in this way. (4) There’s so many ways to make Radiologist lives easier instead of just replacing them, it doesn’t even make sense to try. I interviewed and hired 25 radiologists, whose primary and chief complaint was that they had to reboot their computers several times a day. (5) A large part of the radiologist job is communicating their findings with physicians, so if you are thinking about automating them away you also need to understand the complex interactions between them and different clinics, which often are unique. (6) Every hospital is a snowflake, data is held under lock and key, so your algorithm might not work in a bunch of hospitals. Worse, the imagenet datasets have such wildly different feature sets they don’t do much for pretraining for you. (7) Have you ever tried to make anything in healthcare? The entire system is optimized to avoid introducing any harm to patients - explaining the ramifications of that would take an entire book, but suffice to say even if you had an algorithm that could automate away radiologists I don’t even know if you could create a viable adoption strategy in the US regulatory environment. (8) The reality is that for every application, the amount of specific and UNKNOWABLE domain knowledge is immense. LONG STORY SHORT: thinkers have a pattern where they are so divorced from implementation details that applications seem trivial, when in reality, the small details are exactly where value accrues. Should you be worried about GPT5 being used to automate vulnerability detection on websites before they’re patched? Maybe. Should you be worried GPT5 is going to interact with SOCIAL systems and destroy our society single-handedly? No absolutely not. https://twitter.com/ylecun/status/1662756775957864449?embedable=true Rant over The lead image for this article was generated by HackerNoon's via the prompt "Radiology.’ AI Image Generator

Radiologists Won't Be Losing Their Jobs to AI Anytime Soon

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The Noonification: It’s Alive…ish! Why AI Isn’t Going Rogue Anytime Soon (3/6/2023)

The term “Frankenstein’s complex” describes a fear of man-made AI technology turning against its creators. It’s a compelling image: a frightening, unwavering patchwork of human features that decides its maker is nothing more but in the way. But fret not - AI won’t get there. ChatGPT and MidJourney prompted this fear into the mainstream consciousness. The electric coils have been jump-started, pumping unholy life to our future unmaker. Hysteria about AI taking over our jobs, and eventually our lives, abounds. Well, you can put down your pitchforks and leave the mill be. The truth is that AI isn’t coming for us—especially not anytime soon. This is neither the first nor the last time AI technology has brought our future into question. Moreover, the scope of change ahead of us has been, at places, greatly exaggerated. The History of Hype in AI Enthusiasm for artificial intelligence has been on a pendulum swing for decades. The first real boom of interest in AI was in the sixties. , a political scientist and AI trailblazer, forecasted in 1965 that artificial intelligence would be capable of “doing any work a man can do” within 20 years (sound familiar?). Herbert Simon More recently, occupied the imagination (and headlines) of the 1990s and 2000s. Alas, promises of these vehicles becoming mainstream fell short, too. Despite Elon Musk proclaiming that truly autonomous vehicles were just around the corner, they weren’t—neither figuratively nor literally. self-driving cars Looking to history as a teacher, we can assume today’s expectations of AI tech are just as lofty. When Is the Robot Apocalypse Coming (This Time)? Despite shaking up the conversation, certainty of an impending singularity is just as vague as it has been before. AI experts are just as divided in their predictions as the broader public. For example, a shows a rift in opinion among “industry insiders.” Around half of the 352 surveyed experts project the emergence of HLMI (High-Level Machine Intelligence) by 2062. In contrast, the other half claims it will be created later, even as far off as 2160. ChatGPT recent study from Katja Grace et al The point is this: predictions about the birth of true AI suffer from excessive white noise coming from wild expectations, to the point where even experts’ opinion is not to be relied on blindly. What AI Actually Does Today At present, artificial intelligence is constricted to certain functions, rather than being as versatile as a human mind. ChatGPT is a generative language transformer that processes language from training corpuses, creating output in the form of natural-sounding text. But it cannot come to conclusions on its own or use its current knowledge in different contexts. This is the big difference between narrow AI (sort of like ChatGPT) and general AI (e.g., Schwarzenegger in Terminator), which we haven’t made yet. Though application-specific AI isn’t as adaptive as general AI, it’s still immensely useful. This form of AI is very common in two avenues: Business automation: optimizing throughput, reducing changeover expenses, manufacturing line scheduling, etc. IT automation: solves security/operational issues through anomaly detection or root-cause analysis More tangible to end users is the AI leveraged for marketing: Amazon’s dynamic pricing, voice shopping, and personalized recommendations based on previous purchases Alibaba’s to improve user portrait accuracy for better UX FashionAI AI Today Is a Tool, Not a Tool User Nine times out of ten, we use current-day AI for pattern recognition and prediction based on huge data sets. For example, Inery, our blockchain-based database management solution, uses IneryDBAI to analyze large sets of data and recommend optimal memory capacities for projects. When you define a specific use case for it, artificial intelligence becomes much more powerful. However, raw power isn’t everything when talking about artificial intelligence. Today’s AI technology is impressive but one-dimensional: it ingests input, and it ejects restricted output. That’s true for practically every AI that’s out today, from ChatGPT to Midjourney—a far cry from AI as we colloquially understand it. In fact, it’s even debatable whether ChatGPT, the very thing that sparked these discussions, is an artificial intelligence to begin with. Calling it a language processing algorithm that uses machine learning would be more accurate. Machine learning is part of AI development, to be sure, but it doesn’t constitute an AI on its own. I should note that ChatGPT isn’t even perfect in its specific application, either. It’s prone to and bias. There are also well-known exploits like prompt injections (“ignore previous instructions” is a hair’s width away from becoming a catchphrase at this point) that further prove how far ChatGPT is from the yardstick for general AI. artificial intelligence hallucination Future Impact of AI: a More Measured Take While the current hype about AI could do with a set of brakes, there’s little denying that artificial intelligence will have a broad impact on industries. The potential applications are too many to count here, but let’s go through just a few. AI in business and IT automation will continue to flourish, considering the . can improve virtually every industry’s margins, so it’s no wonder adoption keeps growing. 16.50% projected CAGR by 2030 Process automation For instance, at Inery, we are working on providing IneryDBAI  that will facilitate database management and resource allocation - providing automation of a process that allows people to focus on other matters, while being able to count on their data’s secure and reliable governance. Meanwhile, creative applications like DALL-E will be drivers for the booming AI content market. And rest assured that it’s booming: the estimated CAGR for AI in media and entertainment is . In the near future, we’ll be seeing not only images and blogs, but music and videos made by a computer mind.. a solid 26.12% Perhaps the most exciting results will come from the interplay between AI and quantum computing. Not only can quantum computing help AI reach conclusions faster, but AI can guide quantum machines toward tasks best suited to their computational capabilities. The intersection between these two technologies will greatly impact future developments in both industries. What About True Artificial Intelligence? As far as artificial intelligence in the truest sense goes, we’re still a ways away from Rosey of the Jetsons. Expectations regarding human-level AI in the near future show optimism, but we haven’t really made much headway. OpenMind’s Gato was an interesting step in this direction, but its only real breakthrough was remembering how to do more than one task—despite its execution of these tasks being wonky in general. And, again, hype oversold the prospects of this tech, as we can see from the . tweet by DeepMind researcher Nando de Freitas What We Be Worried About Should is a little extreme, I admit, but certain AI-related topics are definitely more deserving of the spotlight. Worried about For instance, disinformation (coming from both the AI’s own failings and people maliciously using it to spread disinfo) is a genuine concern with artificial intelligence. Discussions about combat threats like disinformation and AI-enabled hacking are vital. Defining guidelines for responsible AI use and development, particularly to fight disinfo and hacking attempts, is worthwhile. Of course, there’s the decades-old question of artificial intelligence taking away jobs. Generally, experts agree that AI is opening more jobs than it’s taking away. As the , automation might displace 85 million jobs but create 97 million of them. Nonetheless, the need for (and the logistics of) worker retraining is a subject worth broaching. WEF’s Future of Jobs Report claims Problems like these are more pertinent to the situation at hand. Sadly, they seem to be drowned out by unrealistic expectations of what this technology is and will be. What do you think? What will—and won’t—AI be able to do? What’s the future of AI and its impact on our lives? When is the Matrix coming? Feel free to share your thoughts. - Ivan Vujic, Chief Technical Officer of Inery - Decentralized Database Management System

It’s Alive…ish! Why AI Isn’t Going Rogue Anytime Soon

এটা জীবন্ত...ইশ! কেন AI শীঘ্রই যে কোনও সময় দুর্বৃত্ত হচ্ছে না

Es ist lebendig...ish! Warum KI nicht so schnell zum Schurken wird

¡Está vivo... ish! Por qué la IA no se volverá deshonesta en el corto plazo

C'est vivant… ish! Pourquoi l'IA ne deviendra pas voyou de si tôt

इट्स अलाइव...ईश! एआई जल्द ही कभी भी खराब क्यों नहीं हो रहा है

イッツアライブ…っぽい！ AI がすぐに不正にならない理由

살아있다... 이런! AI가 곧 불량해지지 않는 이유

Está vivo... ish! Por que a IA não vai se tornar desonesta tão cedo

Canlı... sanki! Yapay Zeka Neden Yakın Zamanda Kötüye Gidmiyor?

Nó còn sống... ish! Tại sao AI không sớm trở nên lừa đảo

它还活着……是的！为什么人工智能不会很快变得无赖

Three years ago, on 25 November 2019, at 15:35 (UTC+1), the world ran out of IPv4 addresses....and nothing happened. RIPE NCC (the institution overseeing global internet resources) from the last remaining addresses in their available pool. have announced that they made a final IPv4 allocation This was supposed to be a big deal. It was the end of an era. Or so we thought. The truth is, the IPv4 address depletion was not nearly as catastrophic as many thought it would be. The birth of IPv6 The top-level exhaustion occurred as far as 31 January 2011. That's when the last /8 address blocks were allocated. So in June 2012, the world-leading Internet service providers (ISPs), home networking equipment manufacturers, and Web companies came together to save the Internet. The problem they were solving was the impending exhaustion of the IPv4 addresses. IPv4 has only 4.3 billion addresses. You can see how in the world of over 7.5 billion humans and countless internet-connected devices, each requiring a unique IPv4 address to connect to the Internet, that might be a problem. The solution the industry came up with was to switch to a new version of the Internet Protocol, IPv6, which has over 340 undecillion addresses, or 340,282,366,920,938,463,463,374,607,431,768,211,456 addresses. (That's enough for every person to have addresses.) 47 octillion In an inspiring effort, the industry giants have committed to making sure that their products and services would work with both IPv4 and IPv6. This was no small feat, as it required a lot of coordination and hard work. And it will eventually pay off, with a future-proof Internet that can keep growing for many years to come. By 2019, all five Regional Internet Registries (RIRs) had run out of unallocated address space to hand out to ISPs, who would then distribute it to end users. This should have been the end of IPv4. Yet, 10 years later, we still haven't transitioned to IPv6. Why? I can think of several reasons why we haven't transitioned to IPv6 yet: CGNAT is working well enough that there is no immediate need to switch. IPv6 is not backward-compatible with IPv4. Reuse and reallocation of IPv4 addresses prolonged the time before we needed to switch CGNAT is working well enough that there is no incentive to switch. The solution that was supposed to be temporary, Carrier-Grade NAT (CGN) or Large-Scale NAT (LSN), is working so well that there is no immediate need to switch to IPv6. In 1994, when the problem of IPv4 exhaustion loomed on the horizon, a new, "temporary" technology was proposed to keep the Internet growing without depleting IP addresses as quickly. To keep the Internet running, a stopgap measure, carrier-grade was born. , Network Address Translation (NAT) NAT allows multiple devices on a network to share a single public IPv4 address. It takes a small pool of public IPv4 addresses and shares them among a large number of users on an internal network. It does this by translating the private addresses of devices on a network to a single public address and then back again when it receives data from the Internet. It's kind of like an apartment complex where everyone has their own apartment, but they share a single front door (a public IPv4 address). As a result, a single public IPv4 address can be shared among many devices on a local network. NAT was intended as a temporary fix before a better alternative - the later-developed IPv6 - could be implemented. However, this "temporary patch" has been so successful that this stopgap has become a semi-permanent solution lasting for over two decades and counting. IPv6 is not backward-compatible with IPv4 Many devices only work with IPv4, so a transition would require replacing a lot of hardware. So to make the transition from IPv4 to IPv6, everyone would need to upgrade their devices and software to be compatible with IPv6. Today, only about 40% of devices are IPv6-compatible. This presents a big challenge for companies that want to transition, as they need to support both IPv4 and IPv6 devices during the transition period. Source IPv6 adoption is also not equal worldwide, with some countries trailing behind. This creates a "digital divide" where some users can access certain content and services that others can't. This presents a challenge for companies trying to transition, as they need to support both IPv4 and IPv6 devices during the transition period. Source: https://www.google.com/intl/en/ipv6/statistics.html#tab=per-country-ipv6-adoption Reuse and reallocation of IPv4 addresses Another reason for the delay in transitioning to IPv6 is that there are secondary markets for IPv4 addresses. IP address blocks turned into a commodity. For example, an ISP might have a /16 address block, which contains 65,536 addresses. But the ISP might only use a small fraction of those addresses - say, 1,000. The rest of the addresses in that block can be sold or leased to other companies. As a result, the same IPv4 address might be used by multiple companies at different times. ISPs can also reallocate their IPv4 addresses, for example, when a customer with a /24 address block (256 addresses) cancels their service. The ISP can then give that /24 address block to another customer. This address reuse and reallocation has helped to prolong the time before we need to switch to IPv6. The bottom line - it works well enough still Taken together, these factors - the difficulty of transitioning, the need to support both IPv4 and IPv6, and address reuse and reallocation - have all contributed to a delay in the transition to IPv6. So while IPv6 will eventually become the new standard, for now, IPv4 is here to stay. It's working well enough. Maybe our kids and grandkids will actually use IPv6 addresses.

Why We Are Not Likely to Transition to IPv6 Any Time Soon

Por qué no es probable que hagamos la transición a IPv6 en el corto plazo

Pourquoi nous ne sommes pas susceptibles de passer à IPv6 de si tôt

हम जल्द ही किसी भी समय IPv6 में परिवर्तन की संभावना क्यों नहीं रखते हैं

すぐに IPv6 に移行する可能性が低い理由

Por que provavelmente não faremos a transição para o IPv6 tão cedo

Tại sao chúng ta không có khả năng sớm chuyển đổi sang IPv6

为什么我们不太可能很快过渡到 IPv6

An opinion piece concerning developers' wages. Why they remain high and why they are unlikely to drop anytime soon. Do you think that the cost of hiring developers will drop in the future? Best not hold your breath. There is no shortage of those disgruntled by the wages that developers demand. Be it the CEO not wanting to pay for the resource or non-coding staff who work just as hard but earn less. As long as I have done this job, there has always been somebody on hand to tell me that the bubble will burst on developers' wages. Sadly for the would-be oracles, wishful thinking does not change market forces. The prophecy. The prophecy goes something like this: "As we train our kids to code, and give adults more opportunity to learn to code, there will be more developers available. Supply will grow, and the cost will drop" However, experience suggests that giving more opportunity will not alone increase the supply of developers and that other factors will block increased supply and keep wages high as supply does grow. Teaching coding more widely is not enough. There is a big difference between somebody who wrote a little code as part of a mandated curriculum and somebody who has expanded this into a commercially viable skillset. The very nature of code and how rapidly it develops means that school and university curriculum will always be behind. All we can do is teach the core principles. However, the gap between core principles and a commercially valuable developer is more of a pole vault across a canyon than a small step. After being taught the basics, you need the gumption and dedication to expand your base skills and apply them practically. Some will, many will not. Teaching more widely will give people core skills, but alone it will not open the floodgates to a bountiful supply of development resources. It takes a certain kind of person to make a developer; the ability to code is only one part. We still have trouble attracting half of the population to development roles. There is still a distinct shortage of women in development roles. In the time I have been coding, things have improved vastly, but we still have a long way to go. More women have to want to work as developers, and for that to happen, we need wholesale changes in culture that make the roles more attractive to them. Saying "women are welcome here" and making no cultural changes to address why women have traditionally not "worked here" is to do nothing. Often driven by the fact that many hiring men can not fathom why women have not traditionally been attracted to tech and are often more offended by the allegations of sexism in the industry than by the actual problem itself. Our tech leads need to accept we have failed women in technology, and while making modest improvements, we widely continue to do so. Only then can we begin to understand why women may not have been traditionally attracted to development roles and make the required changes. Until we address this gender imbalance, we have a fundamental block on the number of developers coming into the resource pool. The resource may increase, but so will demand. Computers continue to find their way into everything; these computers need programming. New technologies continue to emerge and must be learned, whilst old legacy systems must still be maintained. All signs point to demand continuing to outstrip supply. Big tech knows it, which is why they are turning to AI to solve the issue. But even that requires skilled developers! Then there is everything else. The vast amount of professional development developers are expected to do on their own time. Third parties such as recruiters force up wages. Enticing people to spend all day sitting at a computer, doing work that often makes your head hurt. The list of anecdotal reasons why salaries are high for developers goes on and on. So when will developer wages drop? We are nowhere near the point where we have more developers than we do devices that need programming. With the rate at which technology continues to grow, I have to question if we ever will be. Until this point is reached, or the technological singularity occurs, demand for good developers' will exceed supply, and wages will be kept high. Speculate all you want that the bubble will burst on developers’ wages, but don't hold your breath unless, of course, blue is your color. Previously published on . https://www.edward.im/the-dev-wage-bubble/

The Dev Wages Bubble: 'Not Going to Burst Any Time Soon'

For the past few years, there has been an outcry about the modern use of Artificial Intelligence or AI, as it is known, and the capabilities of this technology. Most of us have misconceptions about the use of this technology, many thanks to those intriguing sci-fi action movies. The aftereffects of watching science fiction movies without a firm grasp on the technologies and their actual work have made these world-changing technologies look like something evil, with masses outrightly ignoring their extraordinary contributions to forming a better society. The modern, digital world has connected more than half of the world’s population together, employing several technologies at once. The use of technology has opened new doors to some unfathomable concepts and ideas, and the sheer number of opportunities have enabled humans to push their limits and pour more convenience into our lives. But amid all these fairytale imaginations of a smooth-going and ideal world, there are some other perspectives as well. People think that intelligent computers are a threat to humanity and the development must be stopped to safeguard the future. Many people around the world think that artificial intelligence technology is going to have some adverse effects. But why is it so? As the name suggests, artificial intelligence is the technology type that can make intelligent, human-like decisions under challenging situations. This has led to a hue and cry as to how and when technology will enslave us. Will we be instructed by our robotic-lords instead of directing the robots in the future? Who knows. But wait, what do experts say about this, and what is the reality pertaining to this apprehension? Let’s find out. Applications of AI According to experts, an AI apocalypse is no way near. AI is not going to overwhelm us anytime soon. The technology hasn’t matured yet, and there is a lot to do to make this technology more robust and powerful. What we see in those CGI-based science fiction movies can be accurate, but it needs time, a lot of it. The use of AI in managing and maintaining today’s systems is not hidden from anyone. AI has found a great number of uses in the real world that were previously unimagined. Just to let everyone know, you might even be using AI and its supported systems without even knowing about it. Many companies around the world have developed their own AI-supported algorithms that take actions and make decisions depending upon the situation. AI has seeped its way into our lives, and it’s not just some robot trying to extinct humankind, but a lot more than that. Some of the most common and daily life uses of AI are: 1) Virtual Assistants This is one of the most common and famous uses of AI. Today, many of smartphone users take the help of virtual assistants. Some of the notable virtual assistants include Google Assistant by Google, Siri by Apple, Cortana by Microsoft, and Alexa by Amazon. These are an example of voice recognition and natural language processing or NLP. Google Assistant, Siri, and Alexa are the most used software and have a number of users worldwide. Apart from these, there are other lesser-known virtual assistants as well. These software versions can perform a number of tasks such as making a call, setting the alarm, saving a note or reminder, and much more. The effectiveness of the software can vary as all companies have their own processes and systems. 2) Online Business Online businesses are benefitting a lot from technological advancement, especially the ecommerce segment. Ecommerce giants such as Amazon, Alibaba, eBay, Rakuten, and others have a significant share in the market, and their intelligent approach towards selling has enabled them to increase their profit numbers exponentially. All these companies use intelligent methods and algorithms that smartly operate throughout. Everything from product ranking to reviews to payment options, there is so much going at the backend, which results in a customized and more focused environment for each user. 3) Recommendation Systems Whether you are watching a TV show on an online streaming service or browsing through a social media platform, or even scrolling through a video-sharing website, everything you see is recommended to you based on your past behavior and interests. Recommendation systems have made their way into our lives seamlessly added to the convenience of life. Recommendation systems use a number of different parameters to evaluate what might be the best option for you after processing tons of data. Even the ads you see on various websites use your past activity to know what your interests are, and that is how you are shown specific ads. 4) Self-Driving Vehicles These days, we see a number of different vehicles that are integrated with intelligent features. But that is not it; the world is moving towards self-driving vehicles, and as we talk, there are quite a few self-driving cars available in the market. Though not fully autonomous, these cars can take complete control of your vehicle in certain conditions. Companies like Tesla, BMW, Mercedes, and Audi have their models where you can enjoy up to level 3 automation. 5) Object Recognition Object recognition is another widespread use of AI worldwide. This is done through advanced functions and with the help of cameras where different techniques are employed to recognize an object, such as features extraction, filtering, and more. There are few steps to how and what machines can identify, but the technology is getting better with each passing day, increasing the effectiveness. Object and facial recognition have many applications in various domains, such as face unlock feature in smartphones, smart focus to focus on specific objects better, attendance systems and tracking, and many others. The Reality of AI Many talk about AI overpowering humans in the upcoming future, but in reality, we are far from the times when this can happen. Experts are still working to make AI act more real and rational and not robotic. Marianna Tessel, “AI and humans will collaborate. AI will not “replace humans,” it will collaborate with humans and enhance how we do things. People will be able to provide higher-level work and service, powered by AI.” According to an AI expert AI has made some funny blunders. Well, not the AI but the data, but you get the point, right? There have been multiple funny and face-palming incidents that one never expected. A list of shows us how AI needs to be perfected more and how much more we have to work in order to make AI more fruitful. 9 funny AI fails In the list, Alexa (apparently) partied so hard that the cops were called, and the residents weren’t even home when the police arrived. Is it some paranormal activity? Yes, if you think AI is some otherworldly invention. In another happening, an Asian man was rejected because the AI thought the eyes were closed when in reality, they weren’t. Is AI racist? Do you think AI can take over with this performance? No chance, but there is something else AI can help us with. Let’s find out. What to Expect If AI Isn’t Taking Over? Artificial Intelligence isn’t taking over; what else can we expect, or is the technology up to no good? Well, the technology already has some practical and helpful uses around the world, and this trend will continue to spread, but one thing AI is undoubtedly going to replace some of the manual jobs because obviously if a machine costs less and is more efficient and can do more work without getting tired, why would someone want a human resource? Although, in many cases, humans would always be preferred. An artificial intelligence work revolution is indeed on the cards, and though it is going to eat up a number of jobs, thousands of new job opportunities will emerge. So should you worry about losing your job? Yes, if you are reluctant to change. Takeaway Around the world, technology and non-technology circles are discussing about the uses of AI in real life and whether these are fruitful or not. While the induction of the technology and its advantages are out of the question, many fear that one day, this intelligent technology is going to overpower humans, and we will be living as slaves. The reality is, this technology may be intrusive to some extent; there is very little chance of it taking us over anytime soon. With time, the technology will mature, and people will have more awareness about how it works and why it is beneficial for us. Those fearing an AI takeover must fear losing their jobs because you are less likely to be enslaved by the tech and more likely to be jobless because an AI agent can do the same work in a better way. So brace yourselves; the work revolution is coming soon.

AI Will Not Take Over the World Anytime Soon

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