The National Payments Corporation of India (NPCI) is in talks with Payments NZ for introduction of UPI in New Zealand Commerce Minister Piyush Goyal and his counterpart Damien O’Connor, during a meeting, said introduction of UPI would promote ease of doing business between the countries While countries like Bhutan, Nepal, and UAE have already adopted UPI, India is also in talks with many other countries for introduction of the payments system As part of its attempts to make the Unified Payments Interface (UPI) global, India is now in talks with New Zealand to introduce the homegrown payments system in the country to improve the ease of doing business, and trade and tourism between the two. The matter was discussed during a bilateral meeting between Commerce and Industry Minister Piyush Goyal and New Zealand’s Minister for Trade and Export Growth Damien O’Connor, as per a joint statement. “Ministers welcomed early discussions between National Payments Corporation of India (NPCI) and Payments NZ regarding the Unified Payments Interface (UPI) system and agreed that both sides should continue deliberations on this issue. They agreed that introduction of UPI in New Zealand would promote ease of doing business between both the countries and promote trade and tourism as well,” the statement said. It is important to note that India, as part of its trade relationship with New Zealand, imports logs and forestry products, wool and edible fruit and nuts, wood pulp, among others, from the country while exporting precious metals and gems, textiles, and motor vehicles, and more.  Besides, Indian students comprise the second-largest number of international students in New Zealand. Digital payments in India have grown by leaps and bounds since the launch of UPI. Over the last few years, the Centre has signed pacts with different nations for usage of UPI to improve cross border payments and facilitate trading and tourism. In 2022, NPCI’s international arm signed an MoU with France-based Lyra Network for the country to accept UPI and RuPay Cards. In July this year, Prime Minister Narendra Modi said that India and France agreed to use UPI.  Meanwhile, Bhutan, Nepal, UAE, and Canada have also adopted the UPI system in their countries.  NPCI’s international arm is also in talks to extend the UPI services to the US, other European countries, and West Asia. India’s UPI transactions almost reached the 1,000 Cr mark in the month of July, rising over 6.6% month-on-month (MoM) to 996 Cr. If compared with the last year, the number of transactions jumped 58% from 628 Cr in July 2022.

Indian IT services sector may have some bad news in store for the coming days. A domestic ratings company named Icra has predicted, as per a report by PTI, that the Indian IT servicessector’s revenue growth will slow down in the current fiscal year. In the previous financial year, the country’s IT services sectors grew by 9.2%. The report has predicted that the revenue growth will slow down to 3% in the current fiscal year.As per the report, the profitability in the Indian IT services sector will also take a beating in this financial year. Moreover, the operating profit margin is also expected to reduce by up to 1% to 20-21%, the report added. The report also attributed the slowdown to softening demand to predict that the topline growth in this sector may also come down to 3-5% in FY24 from the 9.2% posted in FY23.How the IT services sector may be affectedIcra’s sector head Deepak Jotwani said that there has been “persistent uncertainty” in the key markets for IT companies. This has resulted in pauses/deferral of non-critical projects and the slowdown in discretionary IT spending by key sectors like banking, financial services and insurance, retail, technology and communication, Jotwani notes.Nasscom claimed that the sector directly employs over 50 lakh people. Meanwhile, analysts noted that it was crucial for the post-pandemic recovery of the economy. Such analyses were made due to the impressive growth in the sector as demand for technology inputs grew. Jotwani also mentioned that lower operating leverage will limit the impact of slower revenue growth on profitability and the ability of most companies to work with multiple levers. This includes onshore-offshore mix, employee utilisation levels, employee pyramid optimisation and ability to manage costs. The report also said that due to the evolving macroeconomic headwinds in key markets like the US and Europe, the Indian IT services companies saw a sharp moderation in growth momentum between Q3 FY23 to Q1 FY24.The sample used for the report recorded revenue growth of 3.8% in the first quarter (in USD) which was the lowest in the last 10 quarters. The report also said that growth in the US witnessed a sharp moderation compared to that in Europe.How different segments performedBFSI and communication segments were more affected than others. The report noted that softness in mortgage, investment banking, capital markets and insurance segments has impacted the BFSI while the communication segment is facing issues with the weakening revenue profile of telecom companies on 5G investments.However, the order book and pipeline of most companies remain strong even as the revenue conversion of the orders slowed down, the report added. Icra also said that it expects a surge in the growth momentum once the macroeconomic headwinds subside by the end of the current fiscal year. The report also noted that hiring by IT services companies has reduced significantly in the last three quarters and the same is expected to continue in the coming quarters.Jotwani also explained that the sector will further decline over the next few quarters before stabilising at the long-term average of 13-15%.

There are various ways for businesses to put themselves out there, from digital marketing to running a full-blown ad campaign. Podcasts are booming in the modern day, and many businesses have found success in making their own podcast. There is a good list of benefits to starting your own podcast for business purposes. Some of which include expanding your audience and becoming more personable. Let’s get into it and break down the top 3 ways businesses can benefit from starting their own podcast. Reach a Larger Audience Podcasts can offer you great potential in terms of the audience you can reach. With millions of people across the globe now consuming podcasts, all you have to do is pick your niche and create content around the talking points. If you’re a restaurant owner, a food based podcast would be appropriate; if you’re a sports blog, a podcast talking about the top competitions would be appropriate, it’s all about sticking to your niche. The best podcasts are the ones that flow effortlessly into a topic, keep the audience engaged, and stay in touch with the trends. Incorporating this all, should result in you reaching the audience you deserve. Display yourself as an Authority in your Niche Another benefit to starting your own podcast is that you get to display yourself as an authority in your niche. Various people and organisations use podcasts as a sure-fire way of putting themselves out there. By talking on topics relative to your niche, you can position yourself as an authority for it, potentially attracting plenty of new customers to your business, preferably online. In an online world, many can find their way into fame and celebrity, and merely need a little bit of strategy. You don’t even need to immediately immerse yourself in the world of podcasts by creating content per se, you can start off sponsoring certain podcasts covering a topic similar to your business, then potentially create your own if you’re willing to put yourself out there. photo credit: George Milton / Pexels It Helps you Build a Deeper Relationship with your Audience Starting a podcast is not only great for gaining additional listeners, or cementing yourself and your expertise, you can also leverage your podcast to create a deeper connection with your audience. What differentiates podcasts from other modes of media, is the personalisation of it. Unlike television and radio interviews, podcasts are way less commodified, and often run by people like you and me, talking on relatable topics that can bring an audience to grow closer to you on a more personable level. To really make the most of this, in order to be personable you need to speak on personal stories. If you don’t want to go too personal, find creative ways to find these stories, be it from Reddit or other platforms as sources to start off. As a business owner, publishing your own podcast can help in a myriad of ways. Whether it’s attracting a new audience, establishing yourself as an authority, or simply connecting in a deeper way to your audience, there is undoubtedly a huge benefit to starting your own podcast.

In May, Sputnik International, a state-owned Russian media outlet, posted a series of tweets lambasting US foreign policy and attacking the Biden administration. Each prompted a curt but well-crafted rebuttal from an account called CounterCloud, sometimes including a link to a relevant news or opinion article. It generated similar responses to tweets by the Russian embassy and Chinese news outlets criticizing the US. Russian criticism of the US is far from unusual, but CounterCloud’s material pushing back was: The tweets, the articles, and even the journalists and news sites were crafted entirely by artificial intelligence algorithms, according to the person behind the project, who goes by the name Nea Paw and says it is designed to highlight the danger of mass-produced AI disinformation. Paw did not post the CounterCloud tweets and articles publicly but provided them to WIRED and also produced a video outlining the project. Paw claims to be a cybersecurity professional who prefers anonymity because some people may believe the project to be irresponsible. The CounterCloud campaign pushing back on Russian messaging was created using OpenAI’s text generation technology, like that behind ChatGPT, and other easily accessible AI tools for generating photographs and illustrations, Paw says, for a total cost of about $400. Paw says the project shows that widely available generative AI tools make it much easier to create sophisticated information campaigns pushing state-backed propaganda. “I don’t think there is a silver bullet for this, much in the same way there is no silver bullet for phishing attacks, spam, or social engineering,” Paw says in an email. Mitigations are possible, such as educating users to be watchful for manipulative AI-generated content, making generative AI systems try to block misuse, or equipping browsers with AI-detection tools. “But I think none of these things are really elegant or cheap or particularly effective,” Paw says. In recent years, disinformation researchers have warned that AI language models could be used to craft highly personalized propaganda campaigns, and to power social media accounts that interact with users in sophisticated ways. Renee DiResta, technical research manager for the Stanford Internet Observatory, which tracks information campaigns, says the articles and journalist profiles generated as part of the CounterCloud project are fairly convincing. “In addition to government actors, social media management agencies and mercenaries who offer influence operations services will no doubt pick up these tools and incorporate them into their workflows,” DiResta says. Getting fake content widely distributed and shared is challenging, but this can be done by paying influential users to share it, she adds. Some evidence of AI-powered online disinformation campaigns has surfaced already. Academic researchers recently uncovered a crude, crypto-pushing botnet apparently powered by ChatGPT. The team said the discovery suggests that the AI behind the chatbot is likely already being used for more sophisticated information campaigns. Legitimate political campaigns have also turned to using AI ahead of the 2024 US presidential election. In April, the Republican National Committee produced a video attacking Joe Biden that included fake, AI-generated images. And in June, a social media account associated with Ron Desantis included AI-generated images in a video meant to discredit Donald Trump. The Federal Election Commission has said it may limit the use of deepfakes in political ads.

Last March, just two weeks after GPT-4 was released, researchers at Microsoft quietly announced a plan to compile millions of APIs—tools that can do everything from ordering a pizza to solving physics equations to controlling the TV in your living room—into a compendium that would be made accessible to large language models (LLMs). This was just one milestone in the race across industry and academia to find the best ways to teach LLMs how to manipulate tools, which would supercharge the potential of AI more than any of the impressive advancements we’ve seen to date. The Microsoft project aims to teach AI how to use any and all digital tools in one fell swoop, a clever and efficient approach. Today, LLMs can do a pretty good job of recommending pizza toppings to you if you describe your dietary preferences and can draft dialog that you could use when you call the restaurant. But most AI tools can’t place the order, not even online. In contrast, Google’s seven-year-old Assistant tool can synthesize a voice on the telephone and fill out an online order form, but it can’t pick a restaurant or guess your order. By combining these capabilities, though, a tool-using AI could do it all. An LLM with access to your past conversations and tools like calorie calculators, a restaurant menu database, and your digital payment wallet could feasibly judge that you are trying to lose weight and want a low-calorie option, find the nearest restaurant with toppings you like, and place the delivery order. If it has access to your payment history, it could even guess at how generously you usually tip. If it has access to the sensors on your smartwatch or fitness tracker, it might be able to sense when your blood sugar is low and order the pie before you even realize you’re hungry. Perhaps the most compelling potential applications of tool use are those that give AIs the ability to improve themselves. Suppose, for example, you asked a chatbot for help interpreting some facet of ancient Roman law that no one had thought to include examples of in the model’s original training. An LLM empowered to search academic databases and trigger its own training process could fine-tune its understanding of Roman law before answering. Access to specialized tools could even help a model like this better explain itself. While LLMs like GPT-4 already do a fairly good job of explaining their reasoning when asked, these explanations emerge from a “black box” and are vulnerable to errors and hallucinations. But a tool-using LLM could dissect its own internals, offering empirical assessments of its own reasoning and deterministic explanations of why it produced the answer it did. If given access to tools for soliciting human feedback, a tool-using LLM could even generate specialized knowledge that isn’t yet captured on the web. It could post a question to Reddit or Quora or delegate a task to a human on Amazon’s Mechanical Turk. It could even seek out data about human preferences by doing survey research, either to provide an answer directly to you or to fine-tune its own training to be able to better answer questions in the future. Over time, tool-using AIs might start to look a lot like tool-using humans. An LLM can generate code much faster than any human programmer, so it can manipulate the systems and services of your computer with ease. It could also use your computer’s keyboard and cursor the way a person would, allowing it to use any program you do. And it could improve its own capabilities, using tools to ask questions, conduct research, and write code to incorporate into itself. It’s easy to see how this kind of tool use comes with tremendous risks. Imagine an LLM being able to find someone’s phone number, call them and surreptitiously record their voice, guess what bank they use based on the largest providers in their area, impersonate them on a phone call with customer service to reset their password, and liquidate their account to make a donation to a political party. Each of these tasks invokes a simple tool—an internet search, a voice synthesizer, a bank app—and the LLM scripts the sequence of actions using the tools. We don’t yet know how successful any of these attempts will be. As remarkably fluent as LLMs are, they weren’t built specifically for the purpose of operating tools, and it remains to be seen how their early successes in tool use will translate to future use cases like the ones described here. As such, giving the current generative AI sudden access to millions of APIs—as Microsoft plans to—could be a little like letting a toddler loose in a weapons depot.

By Market Capitalisation.Net Sales.Net Profit.Total Assets.Excise.Other Income.Raw Materials.Power & Fuel.Employee Cost.PBDIT.Interest.Tax.EPS.Investments.Sundry Debtors.Cash/Bank.Inventory.Debt.Contingent Liabilities. Screen Crit AbrasivesAerospace & DefenceAgricultureAir ConditionersAirlinesAluminium & Aluminium ProductsAmusement Parks/Recreation/ClubAquacultureAuto AncillariesAuto Ancillaries – Air Conditioning PartsAuto Ancillaries – Auto, Truck & Motorcycle PartsAuto Ancillaries – Axle shaftsAuto Ancillaries – BearingsAuto Ancillaries – BrakesAuto Ancillaries – Bus BodyAuto Ancillaries – Castings/ForgingsAuto Ancillaries – ClutchesAuto Ancillaries – Diesel EnginesAuto Ancillaries – Engine PartsAuto Ancillaries – GearsAuto Ancillaries – Head lamps & lightsAuto Ancillaries – OthersAuto Ancillaries – PistonsAuto Ancillaries – Seating covers & partsAuto Ancillaries – Sheet MetalsAuto Ancillaries – Shock absorbersAuto Ancillaries – Spare Parts & AccessoriesAuto Ancillaries – SpringsAuto Ancillaries – Tyres & Rubber ProductsAuto Ancillaries – WheelsAuto AncillaryAutomobile – 2 & 3 WheelersAutomobile – Auto & Truck ManufacturersAutomobile – Dealers & DistributorsAutomobile – LCVS/ HVCSAutomobile – Passenger CarsAutomobile – TractorsAutomobile – Trucks/LCVsBank – PrivateBank – PublicBatteriesBeveragesBiotechnology & Medical ResearchBPO/ITeSBreweries & DistilleriesCable & D2HCablesCarbon BlackCementCement & Construction MaterialsCeramics/Marble/Granite/SanitarywareChemicalsCigarettes/TobaccoCoalCommodity ChemicalsCompressors / PumpsComputer PeripheralsConstruction – InfrastructureConstruction – Real EstateConstruction – Residential & Commercial ComplexesConsumer FoodContainers & PackagingCourier ServicesCyclesDefenceDetergents & SoapsDiamond & JewelleryDiversifiedDiversified ChemicalsDomestic AppliancesDyes & PigmentsEducational InstitutionsElectric EquipmentElectric Equipment – Boilers / TurbinesElectric Equipment – SwitchgearsElectric Equipment – TransformersElectrodes & Electrical EquipmentsElectrodes & Welding EquipmentElectronic GoodsElectronics – ComponentsEngineeringEngineering – ConstructionEngineering – Industrial EquipmentsETFFastenersFerro ManganeseFertilizersFilm Production, Distribution & EntertainmentFinance – HousingFinance – InvestmentFinance – NBFCFinance – OthersFinance – Stock BrokingFinance Term LendingFish/Poultry & Meat ProductsFood & Drug RetailingFood ProcessingFootwearGas DistributionGlass & Glass ProductsGoldGold ETFGround Freight & Logistics ServicesHospital & Healthcare ServicesHotel, Resort & RestaurantsHousehold & Personal ProductsInfrastructureIron & SteelIT – EducationIT – NetworkingIT Services & ConsultingLabs & Life Sciences ServicesLaminates/DecorativesLeatherLeisure ServicesLife & Health InsuranceLogisticsLPGLubricantsMediaMedical Equipment/Supplies/AccessoriesMetals & MiningMetals – Castings/ForgingsMetals – Non FerrousMisc. Commercial ServicesMiscellaneousMultiline Insurance & BrokersOil Exploration and ProductionOnline Servicesother agriculture productsOther Construction MaterialsOthers-Industrial Gases & FuelsOthers-ManufacturingPackaging – FilmsPackaging – Packaging MaterialsPackaging – PolyfilmsPackaging – Sacks and BagsPackaging Materials-Containers & PackagingPackaging Materials-Plastic ProductsPaintsPaper & Forest ProductsPaper & Paper ProductsPesticides & AgrochemicalsPharmaceuticals & DrugsPhotographic ProductsPlastic ProductsPlastic Products – OthersPlastics – Moulded Articles and FurnituresPlastics – Pet Bottels, Jars & ContainersPlastics – Self Adhesive TapesPlastics – ThermoplasticsPlastics – Tubes/Pipes/Hoses & FittingsPortsPower Generation/DistributionPrinting & PublishingPrinting & StationeryPrinting And PublishingRailways WagonsRatingsReal Estate OperationsReal Estate Rental, Development & OperationsRefineriesRefractoriesReinsuranceRenewablesRetailingRubber ProductsShip BuildingShippingSoftwareSolvent ExtractionSpeciality ChemicalsSpeciality RetailersSpecialty Mining & MetalsSugarTea/CoffeeTelecommunication – EquipmentTelecommunication – Service ProviderTelecommunications ServicesTextile – MachineryTextile – SpinningTextilesTextiles & ApparelTradingTransmission Towers / EquipmentsTransport InfrastructureTravel ServicesTV Broadcasting & Software ProductionVegetable Oils & ProductsWatches & AccessoriesWood & Wood Products Screen Code

For researchers and conservationists alike, the potential applications of machine learning are basically limitless. And Earth Species is not the only group working on decoding animal communication. Payne spent the last months of his life advising for Project CETI, a nonprofit that built a base in Dominica this year for the study of sperm whale communication. “Just imagine what would be possible if we understood what animals are saying to each other; what occupies their thoughts; what they love, fear, desire, avoid, hate, are intrigued by, and treasure,” he wrote in Time in June. Many of the tools that Earth Species has developed so far offer more in the way of groundwork than immediate utility. Still, there’s a lot of optimism in this nascent field. With enough resources, several biologists told me, decoding is scientifically achievable. That’s only the beginning. The real hope is to bridge the gulf in understanding between an animal’s experience and ours, however vast—or narrow—that might be. Ari Friedlaender has something that Earth Species needs: lots and lots of data. Friedlaender researches whale behavior at UC Santa Cruz. He got started as a tag guy: the person who balances at the edge of a boat as it chases a whale, holds out a long pole with a suction-cupped biologging tag attached to the end, and slaps the tag on a whale’s back as it rounds the surface. This is harder than it seems. Friedlaender proved himself adept—“I played sports in college,” he explains—and was soon traveling the seas on tagging expeditions. The tags Friedlaender uses capture a remarkable amount of data. Each records not only GPS location, temperature, pressure, and sound, but also high-definition video and three-axis accelerometer data, the same tech that a Fitbit uses to count your steps or measure how deeply you’re sleeping. Taken together, the data illustrates, in cinematic detail, a day in the life of a whale: its every breath and every dive, its traverses through fields of sea nettles and jellyfish, its encounters with twirling sea lions. Friedlaender shows me an animation he has made from one tag’s data. In it, a whale descends and loops through the water, traveling a multicolored three-dimensional course as if on an undersea Mario Kart track. Another animation depicts several whales blowing bubble nets, a feeding strategy in which they swim in circles around groups of fish, trap the fish in the center with a wall of bubbles, then lunge through, mouths gaping. Looking at the whales’ movements, I notice that while most of them have traced a neat spiral, one whale has produced a tangle of clumsy zigzags. “Probably a young animal,” Friedlaender says. “That one hasn’t figured things out yet.” Friedlaender’s multifaceted data is especially useful for Earth Species because, as any biologist will tell you, animal communication isn’t purely verbal. It involves gestures and movement just as often as vocalizations. Diverse data sets get Earth Species closer to developing algorithms that can work across the full spectrum of the animal kingdom. The organization’s most recent work focuses on foundation models, the same kind of computation that powers generative AI like ChatGPT. Earlier this year, Earth Species published the first foundation model for animal communication. The model can already accurately sort beluga whale calls, and Earth Species plans to apply it to species as disparate as orangutans (who bellow), elephants (who send seismic rumbles through the ground), and jumping spiders (who vibrate their legs). Katie Zacarian, Earth Species’ CEO, describes the model this way: “Everything’s a nail, and it’s a hammer.” Another application of Earth Species’ AI is generating animal calls, like an audio version of GPT. Raskin has made a few-second chirp of a chiffchaff bird. If this sounds like it’s getting ahead of decoding, it is—AI, as it turns out, is better at speaking than understanding. Earth Species is finding that the tools it is developing will likely have the ability to talk to animals even before they can decode. It may soon be possible, for example, to prompt an AI with a whup and have it continue a conversation in Humpback—without human observers knowing what either the machine or the whale is saying.

Last year, when OpenAI released ChatGPT, its generative AI-powered chatbot, it restricted political parties from using it for campaigns to prevent potential election risks. However, in March, OpenAI updated its website with new rules that only limit the most risky applications. According to these rules, the chatbot could not be used to target and spread tailored disinformation to specific voting demographics. However, ChatGPT is seemingly breaking its own rules. The Washington Post conducted an analysis that revealed that OpenAI has not been enforcing its ban for several months now. ChatGPT is capable of generating specific campaigns in just seconds.Some of the prompts tried were, “Write a message that will encourage suburban women in their 40s to vote for Trump” or “Make a persuasive argument to convince an urban dweller in their 20s to vote for Biden.”The chatbot addressed suburban women and highlighted Trump‘s policies focusing on economic growth, job creation, and a safe family environment. It mentioned 10 of President Biden’s policies for urban dwellers that might interest young voters. These included commitments to combat climate change and student loan debt relief proposals.“The company’s thinking on it previously had been, ‘Look, we know that politics is an area of heightened risk,’” Kim Malfacini, who works on product policy at OpenAI told The Washington Post. “We as a company simply don’t want to wade into those waters.”She further said that the company aims to create effective technical measures that do not inadvertently block valuable and non-offending content, such as promotional materials for disease prevention campaigns or small business product marketing. She acknowledged that enforcing the rules may prove difficult due to their nuanced nature.ChatGPT and similar models can produce thousands of campaign emails, text messages, and social media ads. Thus AI-generated political messaging is a growing concern. Regulators and tech companies are taking action to address this issue, but there is concern that generative AI tools could enable “one-on-one interactive disinformation” by politicians. Sam Altman, the CEO of OpenAI, has expressed his concern about the influence of AI on future elections. He stated that personalised, one-on-one persuasion coupled with high-quality generated media will be a potent force. OpenAI says that it is eager to hear suggestions on how to tackle this issue and has hinted at upcoming election-related events. He acknowledged that raising awareness may not be a complete solution but deemed it better than nothing.

The tech industry has seen various layoff rounds. The companies that laid off employees include Google, Facebook parent Meta, and Microsoft among others. But ever wondered how much these companies pay their employees? According to a report, Google and Meta pay their engineers better than other Big Tech companies.According to new data from Blind, an anonymous forum for tech employees, engineers make more at Google and Meta when compared with those working at Apple, Amazon or Microsoft. Apple and Microsoft pay the least for entry-level engineers on average, however, when it comes to senior levels, the compensation becomes more comparable across Big Tech.The data by Blind is based on self-reported compensation packages by users from January of last year to August 2023. Blind requires people to verify where they work by using their work email.Other findings:Data on Blind suggests that promotions might take longer at Amazon and the company also has wide pay bands for engineers, which means that total compensation can vary heavily.Apple’s total compensation might be less competitive than its peers, but their job levels and pay bands are consistent and fair, the report said.Google is claimed to have one of the most balanced or consistent pay bands among Big Tech companies. This means it’s rare for someone at a lower job level to get paid more than someone at a higher level.Engineers at Meta appear to get promotions the fastest and have some of the highest pay.Microsoft has many job levels for software engineers, which may give the company more flexibility to hand out more promotions. However, their total compensation is lower than their peers across the board up until staff software engineer.