Monthly Archives: April 2022

While its well known that artificial intelligence and automation make compliance easier and reduce manual processes, whats less well understood is how to harness the technology to gain a richer experience that sparks innovation in business processes.

When tax departments actually engage with AI, the insights garnered can affect everything from how a company allocates human resources to the very direction a business decides to take in the wake of new tax regulations.

Embrace the robots

Tax departments just entering the automation space often see the technology as a one-way street you set up the solution and it pumps out results. But there is a human side.

To truly embrace the full potential of an AI-enabled tax platform, companies need to develop a give-and-take relationship. That means educating the system about the business so the machine can give back insights about how to make the tax function stronger and more efficient.

In fact, by reducing dependence on tax professionals ability to recall, interpret and apply thousands of sales tax rates and regulations, AI enables companies to work smarter and allot their resources to more forward-thinking projects.

Transform the tax function

AI enables companies to organize huge volumes of data to identify opportunities. For example, robotic process automation can help a business corral and control its tax data. From compliance to internal audits, tax is one area that is still particularly labor-intensive, and it is often highly skilled employees who have to take on those roles. RPA bots ease the burden of highly repetitive manual tasks.

Data visualization and dashboards can spark fresh insights, revealing new opportunities for tax efficiencies. By turning raw data into actionable insights, businesses optimize tax performance and model, predict and influence business decision-making.

Beyond that, automation can help companies understand the role of adjacent businesses in the supply chain. Companies know how things should be taxed in their world, but what about those upstream? Any shift in their tax burden could come funneling downstream. AI can catch that kind of change and turn it into actionable information.

Hire and retain great talent

The benefits above are obvious once you understand how AI works. But there are other boons to utilizing cutting-edge AI-enabled tax solutions, including attracting top talent in an incredibly competitive hiring environment.

In-demand tax professionals are more likely to accept a role in which they spend less time on repetitive, transactional tasks and more time honing their strategic contributions. The opportunity to develop and sharpen skills and expertise related to current business systems and advanced tools also offers recruiting and retention benefits.

Automation has become a necessity, but more than that it has become an opportunity. AI isnt just about reduced compliance risk; its about moving the business forward and putting the tax department at the leading edge.

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AI and tax automation can spark fresh ideas for businesses - Accounting Today

If you look at a photograph of leopards, would you be able to tell which two were related based on their spots?

Unless youre a leopard expert, the answer is most likely not, says Tanya Berger-Wolf, director of the Translational Data Analytics Institute (TDAI) at Ohio State University. But, she says, computers can.

Berger-Wolf and her team are pioneering a new field of study called imageomics. As the name suggests, imageomics uses machine learning to extract biological data from photos and videos of living organisms. Berger-Wolf and her team have recently begun collaborating with researchers studying leopards in India to compare spot patterns of moms and children using algorithms.

Images have become the most abundant source of information now, and we have the technology, too. We have computer vision machine learning, says Berger-Wolf. She compares this technology to the invention of the microscope, offering scientists a completely different way to look at wildlife.

Building on TDAIs open-source platform called Wildbook, which helps wildlife researchers gather and analyze photos, the team is now focusing on generative AI approaches. These programs use existing content to generate meaningful data. In this case, they are attempting to analyze crowdsourced images to make biological traits that humans may naturally miss computable, like the curvature of a fishs fin or a leopards spots. The algorithms scan images of leopards publicly available online, from social media to digitized museum collections.

In simple terms, the algorithms quantify the similarity, she says. The aim is to help wildlife researchers overcome a data deficiency problem and, ultimately, better protect animals at risk of extinction.

Ecologists and other wildlife researchers are currently facing a data crunch its tedious, expensive, and time-consuming for people to spend time in the field monitoring animals. Due to these challenges, 20,054 species on the International Union for Conservation of Natures (IUCN) Red List of Threatened Species are labeled as data deficient, meaning theres not enough information to make a proper assessment of its risk of extinction. As Berger-Wolf sums it up, biologists are making decisions without having good data on what were losing and how fast.

The platform started with supervised learning Berger-Wolf says the computer uses algorithms simpler than Siri to count how many animals are in the image, as well as where it was taken and when, which could contribute to metrics like population counts. Not only can AI do this at a much lower cost than hiring people but also at a faster rate. In August 2021, the platform analyzed 17 million images automatically.

There are also barriers that only a computer can seem to overcome. Humans are not the best ones at figuring out whats the informative aspect, she says, noting how humans are biased in how we see nature, focusing mostly on facial features. Instead, AI can scan for features humans would likely miss, like the color range of the wings on a tiger moth. A March 2022 study found that the human eye couldnt tell male polymorphic wood tiger moth genotypes apart but moth vision models with ultraviolet light sensitivity could.

Thats where all the true innovation in all of this is, Berger-Wolf says. The team is implementing algorithms that create pixel values of patterned animals, like leopards, zebras, and whale sharks, and analyze those hot spots where the pixel values change most its like comparing fingerprints. Having these fingerprints means researchers can track animals non-invasively and without GPS collars, count them to estimate population sizes, understand migration patterns, and more.

As Berger-Wolf points out, population size is the most basic metric of a species well-being. The platform scanned 11,000 images of whale sharks to create hot spots and help researchers identify individual whale sharks and track their movement, which led to updated information about their population size. This new data pushed the IUCN to change the conservation status of the whale shark from vulnerable to endangered in 2016.

There are also algorithms using facial recognition for primates and cats, shown to be about 90 percent accurate, compared to humans being about 42 percent accurate.

Generative AI is still a burgeoning field when it comes to wildlife conservation, but Berger-Wolf is hopeful. For now, the team is cleaning the preliminary data of the leopard hot spots to ensure the results are not data artifacts or flawed and are true biologically meaningful information. If meaningful, the data could teach researchers how species are responding to changing habitats and climates and show us where humans can step in to help.

Read more from the original source:

Wildlife photos are a new treasure trove for AI-driven conservation research - The Verge

Japan's Largest ASIC Company Expands to U.S. Market

SAN JOSE, Calif., April 21, 2022 /PRNewswire/ --MegaChips, the leading custom ASIC company in Japan, today announced the launch of its AI Partner Program, which allows companies to integrate powerful AI capabilities without requiring in-house AI experts, allowing vendors to focus on their key strengths and ensuring top quality for the final product.

The AI Partner Program marks the entry of MegaChips into the global Edge AI chips market, which was valued at $9 billion in 2020, and is projected to reach $59.6 billionby 2030 - an average growth rate of 21.2%.

"The AI chip industry is going through many changes, including a pivot from a saturated data center market to emerging use cases for integrated processors, such as the ones you'd find in smart devices", asserts Adrien Sanchez, Technology & Market Analyst, Computing at Yole Dveloppement(Yole). "Edge AI chips benefit companies by allowing them to analyze data from connected devices without sending massive amounts of data into the cloud, which often results in massive costs and potential security risks." (1)

For systems companies, some benefits of the MegaChips AI Partner Program include a dedicated team of engineers that work collaboratively with customers to identify the best ways to implement desired AI functionalities, custom "proof of concept" demonstrations, and optimization strategy in context of a complete system. For IP and ASSP vendors, MegaChips eliminates the need for hiring in-house back-end chip implementation teams.

MegaChips is also announcing its expansion into the U.S. market after extensive success in Japan. MegaChips is now delivering its full-service ASIC solution in the U.S. and offering off-the-shelf access to industry-standard IP components and secure, inhouse design services along with full manufacturing support.

"MegaChips is thrilled to offer the most turn-key solution for enterprise companies looking to implement AI technology," said Douglas Fairbairn, Director of Business Development. "The expansion to the United States is an excellent opportunity for us to bring our edge AI expertise to some of the most innovative technology companies.Be it sensing, voice and image recognition, or other applications, MegaChips is the first and best choice for implementing Edge AI from ideation to silicon."

About MegaChips LSI USA Corp

MegaChips is one of the world's leading custom ASIC providers for consumer, telecom/network, industrial and automotive applications. Headquartered in Japan, with offices in Silicon Valley and Taiwan, Megachips has over 30 years in business and has successfully completed morethan 1,500 ASIC projects. MegaChips operates as an extension of our customers' design teams, to provide a whole solution from concept-to-silicon and has recently expanded to address the growing global demand for embedded AI solutions. With a strong emphasis on cost effectiveness, delivery schedule, and product quality, MegaChips is ISO9001 certified and ensures the highest levels of intellectual property security.

Follow MegaChips on LinkedIn, Twitter, and Facebook for more information.

All trademarks and product names are the property of their respective companies.

MegaChips Media ContactLauren ChouinardFortyThree, Inc.[emailprotected]831.621.5661

SOURCE MegaChips

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MegaChips Focuses on Edge AI with Custom ASIC Solutions - PR Newswire

Leading AI company hosts 400+ global energy, manufacturing, and government leaders at HyperWerx, its 50-acre proving ground, which brings the physical world together with AI.

AUSTIN, Texas, April 21, 2022 /PRNewswire/ -- SparkCognition, a global leader in artificial intelligence (AI) software solutions perfected for business, will host Time Machine Interactive: AI in the Physical World (TMI22) at their 50-acre AI proving ground, HyperWerx today. TMI22 brings over 400 executives across critical industries such as oil and gas, renewables, manufacturing, national security, and defense to the greater Austin area. Guests will experience AI-enabled interconnected and intelligent physical systems, which include examples of IoT, autonomous flight, augmented reality, and cybersecurity. TMI22 is presented by SparkCognition, Gold Sponsor DLA Piper, Bronze Sponsor Raytheon Technologies, SkyGrid, and SparkCognition Government Systems (SGS).

"In the face of climate change and net-zero initiatives, aging and failing assets, emerging cyberthreats to IT and OT infrastructure, an aging workforce and consequential skill gaps, and data overload, AI has become a necessity for every industry," said Stephen Gold, Chief Marketing Officer of SparkCognition. "At TMI22, we are pleased to welcome leading minds from across these sectors to explore tangible, actionable ways in which organizations can tackle their most critical problems and achieve meaningful bottom-line performance."

TMI22 features speakers from major industries, including:

The technology demonstrations at TMI22 include:

To learn more about SparkCognition, visit http://www.sparkcognition.com.

About SparkCognitionSparkCognition's award-winning AI solutions allow organizations to predict future outcomes, optimize processes, and prevent cyberattacks. We partner with the world's industry leaders to analyze, optimize, and learn from data, augment human intelligence, drive profitable growth, and achieve operational excellence. Our patented AI, machine learning, and natural language technologies lead the industry in innovation and accelerate digital transformation. Our solutions allow organizations to solve critical challengesprevent unexpected downtime, maximize asset performance, optimize prices, and ensure worker safety while avoiding zero-day cyberattacks on essential IT and OT infrastructure. To learn more about how SparkCognition's AI solutions can unlock the power in your data, visit http://www.sparkcognition.com.

SparkCognition Contact InfoCara SchwartzkopfCommunications Manager[emailprotected]251-501-6121

SOURCE SparkCognition

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SparkCognition Hosts World Leaders to Show the Future of AI in Business at Time Machine Interactive Event - PR Newswire

Advertising, particularly online advertising, isnt a surefire way to bolster business. A report from ecommerce analytics platform Glew drives the point home: In 2015, 75% of retailers that spent at least $5,000 on Facebook ads ended up losing money on those ads, with the average return on investment landing around -66.7%. Obviously, thats just one segment retail. But the picture doesnt brighten even after broadening out to all categories of advertising. A 2018 survey of marketers by Rakuten Marketing found that companies waste an estimated 26% of their budgets on inefficient ad channels and strategies.

Jaleh Rezaei, the CEO of Mutiny, believes that the problem doesnt lie with the ads themselves. Rather, she pegs it on static, templated websites that dont match the personalization delivered by ads. When buyers follow on an ad online, they often land on a generic website without a targeted call to action, and soon leave not understanding why they should buy.

I faced the conversion problem firsthand when I ran marketing at Gusto, Rezaei told TechCrunch via email. We were successfully driving top-of-funnel growth through ads and other channels, but it wasnt converting into revenue. We solved this problem by creating a growth engineering team that wrote a lot of custom code to drive customers to buy from optimizing our website and signup form to driving upsell and referrals in-app. But most companies dont have the engineers or know-how to do all that.

That, Rezaei says, is why she co-founded Mutiny, which today announced that it raised $50 million in a Series B round co-led by Tiger Global and Insight Partners at a $600 million valuation.Mutinys platform is designed to plug into a companys data and website, using AI to serve thousands of versions of the site to different users.

Growing revenue is the number one priority of every CEO and C-level executive. Over the past decade, companies like Google, Facebook and LinkedIn, along with an ecosystem of adtech and SEO tooling, have made it easy for companies to get in front of their target buyers online, Rezaei continued. However, now that spending money online has become table stakes, the puck has shifted to focusing on reducing marketing waste and turning those dollars into revenue.

Prior to co-founding San Francisco-based, Y Combinator-backed Mutiny, Rezaei was the director of product marketing at VMware. She went onto join the marketing team at Gusto, a payroll management platform, before serving in advisory roles at Y Combinator and Google.

Mutinys other co-founder, Nikhil Mathew, helped to launch LiveGit, an online tool for real-time music collaboration. He then went on to become a head software engineer at Gusto, where he managed and lead the developer infrastructure team. (Rezaei and Mathew worked together while at Gusto.)

An example of website copy generated by Mutiny.

The idea behind Mutiny was to develop an AI system that can learn from a companys online data to provide guidance on underperforming customer segments, Rezaei says. Specifically, Mutiny recommends segments for personalization and shows companies how others personalized for that segment. It might suggest to an enterprise company, for example, that small startups dont convert well on their website, and then show them how rivals personalized their homepages.

In marketing parlance, convert refers to a visitor completing a desired goal, whether thats purchasing a product or simply volunteering their contact information.

Today, companies can use a longtail of manually-intensive alternatives such as connecting data to A/B testing tools, creating hundreds of landing pages, or hiring growth engineers and data scientists to manually connect and analyze data, ideate and custom build solutions for different customer segments, and measure and iterate in-house, Rezaei said. There are also point solutions that help companies with various aspects of personalization, but they either dont use AI or theyre a managed service that the customer cannot leverage self-serve We are creating a new category that makes it easy for any marketer to create personalized experiences and increase conversion.

Mutiny whose AI also learns from different customers site data can generate copy for a website based on what has worked for another, adjacent brands audience. (Rezaei claims that the data is fully anonymized, never shared nor sold, and compliant with relevant privacy laws.) Via AI startup OpenAIs API, Mutiny taps GPT-3, an AI system that can generate convincingly human-sounding text. While Mutiny initially applied GPT-3 only to site headline suggestions, the company eventually began applying the model to whole-page generation, leveraging Mutinys customer data to tune GPT-3 for the purpose.

Our AI is learning from a proprietary data set of hundreds of standardized, anonymized buyer attributes and the content that leads them to convert. We layer this on top of text data from GPT-3 to generate high-converting website copy [Copy is generated for] segments based on user-selected value props like security and ease of use' Rezaei explained. We also train reinforcement learning algorithms with 150 million data points to predict what content resonates best with each individual buyer.

Mutiny competes with several rivals in the AI-powered website personalization space, including Intellimize and Constructor. But the company has impressive momentum behind it. Mutinys customers include Dropbox, Snowflake, Qualtrics and Carta, and Rezaei claims that roughly 50 million people across more than 3 million companies have seen a website personalized by the platforms AI engine. Revenue is on track to quadruple in fiscal quarter 2022.

Rezaei believes that the skills gap in marketing will be one driver of future growth. That remains to be seen a 2021 Clevertouch Marketing survey found that 72% of companies view marketing talent as more essential than technology. But Rezaei makes the case that few companies, particularly in the startup space, have a competency around converting spend into revenue.

The pandemic has forced most commerce and purchasing online for every type of companies. This is compounded by the funding market where companies are raising mega-rounds, the majority of which is earmarked for rapid growth, Rezaei said. As a result, we are seeing online customer acquisition become a board-level concern even for business-to-business companies with large sales teams. Most companies can hire the talent and access the technology needed to advertise, distribute content and raise awareness online [But] companies [dont] have command of efficient online spend.

Another webpage generated by Mutinys AI engine.

Another burden on Mutiny will be convincing potential customers that its platform can overcome the common limitations of personalization engines. As Paul Roetzer writes for Marketing AI Institute, AI without rich data sets can quickly fall short of accuracy benchmarks especially when executives have unrealistic expectations.

We have invested heavily in our AI engine since our Series A, Rezaei said. The result is a fully guided experience for marketers to drive revenue faster based on whats working for their different buyer segments.

Sequoia Capital, Cowboy Ventures and Uncork Capital also invested in Mutinys Series B, joined by executives from Uber, Visa, Salesforce, Square, Figma, Cond Nast, Carta, Snowflake and Atlassian. The company, whose total capital raised stands at $72 million, plans to more than double the size of its 40-person team by 2023 while invest[ing] heavily in its AI technology.

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Mutiny, which personalizes website copy and headlines using AI, raises $50M - TechCrunch

Research in the field of machine learning and AI, now a key technology in practically every industry and company, is far too voluminous for anyone to read it all. This column aims to collect some of the most relevant recent discoveries and papers particularly in, but not limited to, artificial intelligence and explain why they matter.

This week AI applications have been found in several unexpected niches due to its ability to sort through large amounts of data, or alternatively make sensible predictions based on limited evidence.

Weve seen machine learning models taking on big datasets in biotech and finance, but researchers at ETH Zurich and LMU Munich are applying similar techniques to the data generated by international development aid projects such as disaster relief and housing. The team trained its model on millions of projects (amounting to $2.8 trillion in funding) from the last 20 years, an enormous dataset that is too complex to be manually analyzed in detail.

You can think of the process as an attempt to read an entire library and sort similar books into topic-specific shelves. Our algorithm takes into account 200 different dimensions to determine how similar these 3.2 million projects are to each other an impossible workload for a human being, said study author Malte Toetzke.

Very top-level trends suggest that spending on inclusion and diversity has increased, while climate spending has, surprisingly, decreased in the last few years. You can examine the dataset and trends they analyzed here.

Another area few people think about is the large number of machine parts and components that are produced by various industries at an enormous clip. Some can be reused, some recycled, others must be disposed of responsibly but there are too many for human specialists to go through. German R&D outfit Fraunhofer has developed a machine learning model for identifying parts so they can be put to use instead of heading to the scrap yard.

Image Credits: Fraunhofer

The system relies on more than ordinary camera views, since parts may look similar but be very different, or be identical mechanically but differ visually due to rust or wear. So each part is also weighed and scanned by 3D cameras, and metadata like origin is also included. The model then suggests what it thinks the part is so the human inspecting it doesnt have to start from scratch. Its hoped that tens of thousands of parts will soon be saved, and the processing of millions accelerated, by using this AI-assisted identification method.

Physicists have found an interesting way to bring MLs qualities to bear on a centuries-old problem. Essentially researchers are always looking for ways to show that the equations that govern fluid dynamics (some of which, like Eulers, date to the 18th century) are incomplete that they break at certain extreme values. Using traditional computational techniques this is difficult to do, though not impossible. But researchers at CIT and Hang Seng University in Hong Kong propose a new deep learning method to isolate likely instances of fluid dynamics singularities, while others are applying the technique in other ways to the field. This Quanta article explains this interesting development quite well.

Another centuries-old concept getting an ML layer is kirigami, the art of paper-cutting that many will be familiar with in the context of creating paper snowflakes. The technique goes back centuries in Japan and China in particular, and can produce remarkably complex and flexible structures. Researchers at Argonne National Labs took inspiration from the concept to theorize a 2D material that can retain electronics at microscopic scale but also flex easily.

The team had been doing tens of thousands of experiments with one-six cuts manually, and used that data to train the model. They then used a Department of Energy supercomputer to perform simulations down to the molecular level. In seconds it produced a 10-cut variation with 40% stretchability, far beyond what the team had expected or even tried on their own.

Image Credits: Argonne National Labs

It has figured out things we never told it to figure out. It learned something the way a human learns and used its knowledge to do something different, said project lead Pankaj Rajak. The success has spurred them to increase the complexity and scope of the simulation.

Another interesting extrapolation done by a specially trained AI has a computer vision model reconstructing color data from infrared inputs. Normally a camera capturing IR wouldnt know anything about what color an object was in the visible spectrum. But this experiment found correlations between certain IR bands and visible ones, and created a model to convert images of human faces captured in IR into ones that approximate the visible spectrum.

Its still just a proof of concept, but such spectrum flexibility could be a useful tool in science and photography.

Meanwhile, a new study co-authored by Google AI lead Jeff Dean pushes back against the notion that AI is an environmentally costly endeavor, owing to its high compute requirements. While some research has found that training a large model like OpenAIs GPT-3 can generate carbon dioxide emissions equivalent to that of a small neighborhood, the Google-affiliated study contends that following best practices can reduce machine learning carbon emissions up to 1000x.

The practices in question concern the types of models used, the machines used to train models, mechanization (e.g. computing in the cloud versus on local computers) and map (picking data center locations with the cleanest energy). According to the coauthors, selecting efficient models alone can reduce computation by factors of five to 10, while using processors optimized for machine learning training, such as GPUs, can improve the performance-per-Watt ratio by factors of two to 5.

Any thread of research suggesting that AIs environmental impact can be lessened is cause for celebration, indeed. But it must be pointed out that Google isnt a neutral party. Many of the companys products, from Google Maps to Google Search, rely on models that required large amounts of energy to develop and run.

Mike Cook, a member of the Knives and Paintbrushes open research group, points out that even if the studys estimates are accurate there simply isnt a good reason for a company not to scale up in an energy-inefficient way if it benefits them. While academic groups might pay attention to metrics like carbon impact, companies arent as incentivized in the same way at least currently.

The whole reason were having this conversation to begin with is that companies like Google and OpenAI had effectively infinite funding, and chose to leverage it to build models like GPT-3 and BERT at any cost, because they knew it gave them an advantage, Cook told TechCrunch via email. Overall, I think the paper says some nice stuff and its great if were thinking about efficiency, but the issue isnt a technical one in my opinion we know for a fact that these companies will go big when they need to, they wont restrain themselves, so saying this is now solved forever just feels like an empty line.

The last topic for this week isnt actually about machine learning exactly, but rather what might be a way forward in simulating the brain in a more direct way. EPFL bioinformatics researchers created a mathematical model for creating tons of unique but accurate simulated neurons that could eventually be used to build digital twins of neuroanatomy.

The findings are already enabling Blue Brain to build biologically detailed reconstructions and simulations of the mouse brain, by computationally reconstructing brain regions for simulations which replicate the anatomical properties of neuronal morphologies and include region specific anatomy, said researcher Lida Kanari.

Dont expect sim-brains to make for better AIs this is very much in pursuit of advances in neuroscience but perhaps the insights from simulated neuronal networks may lead to fundamental improvements to the understanding of the processes AI seeks to imitate digitally.

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Deep Science: AI cuts, flows and goes green - TechCrunch

Children and young people are growing up in an increasingly digital age, where technology pervades every aspect of their lives. From robotic toys and social media to the classroom and home, artificial intelligence (AI) is a ubiquitous part of daily life. It's vital therefore that ethical guidelines protect them and ensure they get the best from this emerging technology.

Generation Z, who have grown up with AI, are uniquely placed to offer an insight into the potential issues of AI targeted at children and help create governance guidelines. With that in mind the World Economic Forum has set up the AI Youth Council, a global diverse group comprising young people interested in AI.

Members serve as part of the Generation AI project community and have been central to the creation of the Artificial Intelligence for Children Toolkit, published 29 March 2022. The AI Youth Council is designed to bring together young people from 14 to 21 years of age worldwide to discuss AI ethics and governance.

Born between 1996 and 2012, Generation Z is recognized as the digital native generation. We were raised in an era defined by the internet, a time characterized by massive digitalization: social networks were launched, new technologies were created, and AI began its cross-industry debut.

As a result, Gen Z evolved alongside technology, which impacted our childhood in multiple dimensions. With social media, our methods of interaction changed. Instant connectivity translated to spending time with friends 24/7. We easily absorbed new tech trends, and our education was augmented by the integration of new software.

Similarly, born between 2013-2024, Generation Alpha, the first true AI native generation, is experiencing the effects of AI right now. Kids seamlessly interact with AI chatbots and smart toys, use of IoT devices is second nature, and they are used to real-time information access. The effects of AI on childhood are evident: it makes kids crave optimized experiences and hyper-connectivity, whether at home, in school or with friends.

Jianyu Gao, Columbia University, BS in Computer Science, USA

I was raised on an unregulated internet with minimal literacy in privacy and safety, and the adults around me didnt know how to educate me to protect myself because they were just as ignorant as I was. I did stay out of danger because I knew what I was doing I was lucky, but too many other children were not. The internet has given us the opportunity to connect with people around the world who would otherwise be out of reach, but has also exposed children to disturbing content, harmful ideologies, brainwashing communities and social circles, cyberbullies and online stalkers, predators, or other dangerous elements who might not have had access to them in real life.

As we transition into a post-pandemic world that not only lives with the internet but lives on the internet, I reflect on my childhood as a girl with a laptop with worries for the future, but also with the resolve to do better for the youth that will grow up with AI. If we expect AI to be just as human as we are, then we must learn from my generations experiences growing up with technologies such as the internet and prepare for the prospect that AI will not always learn from the best of us.

Guido Putignano, Bachelor of Engineering and Biomedical Engineering, Politecnico di Milano

One of the first times I got in contact with technology was when I was 12. I went to the shop and I saw strange objects that could make you go into other dimensions. At that time, these devices were a one-hour distraction after my evening homework. From the beginning, I remember that technology being harmful to me. I wasted many days watching videos without being intentional about what I was learning. When I turned 16, I started to use these devices proactively. I started making these devices work for me, rather than the opposite.

I think that the best way to predict the future is to create it yourself. I am an optimist, and I think that personalisation and high-speed connectivity will make anything far better than it is today. In this case, AI systems go from being objects to being subjects. One example is in the fields of education and healthcare. Imagine how awesome it would be to have an AI system that could help you personalise many parts of your life and be at the centre of your growth. That system could track thousands of parameters, making astonishingly accurate predictions of your future self. Those opportunities will be a reality in the future.

Joy Fakude, University of Johannesburg, South Africa

Personally, growing up in South Africa, technology didnt have that great of an impact in my life because I didnt have access to it. The closest I came to a laptop was a toy laptop where I practised maths questions, English sentence construction and played some games. Then I advanced to my first cell phone a BlackBerry which I thought was the coolest thing on planet Earth. I then had access to the internet and social media, however access to WiFi became a serious problem and unfortunately that is the reality for a lot of South African youngsters today.

Not having access to data never mind smart phones or laptops in a world that is speeding into a digital era, many South African teens are left behind not even knowing what AI is or what a digital footprint is, or not even knowing how to protect your data. My biggest concern for future generations of South Africans is that the rapid developments of AI technologies leave them stuck in the mud. That they arent taught and because they arent taught cant adapt and, according to Darwins theory of evolution, dont survive.

Born in 2003, my childhood was situated in the transitional stage from floppy disks and BlackBerry phones to social media powerhouses and streaming services. Most of my early interaction with technology was limited to my Sony camera and Nintendo S4. By the time I was 11, I relented to peer pressure and created an Instagram account. As I pondered how to use my new platform, it seemed natural to present the version of myself that fit my current interests. I used the profile Gracie Dancer to perform self-choreographed dance routines or rave about my new tap shoes. Gracie Singer was where I posted all my off-pitched covers of the latest pop songs.

But what was on the surface an apparently innocuous search for a sense of community began affecting me in a way I didnt expect. As my interests evolved, I felt I was wrong for wanting to try different things. The uncertainty of not knowing who there was behind the screen made me feel as if though I was constantly being watched and judged. I began to fear mistakes at a time in my life when they should have been the most welcomed.

While technology has undeniable potential, I worry that the coming generation of children are growing up in a society where we are understood by others solely through our internet personas. Genuine relationships, interests, and activities will come second to keeping up the illusion of perfection, which so often means conformity.

Ecem Yilmazhaliloglu from Turkey, studying at Stanford University

As the last generation to learn navigating bulky, old system units in computer lessons at school and the first generation to grow up having a Facebook profile, I belong to what Id like to call the transition generation. As the new technologies social media, touch screens, cloud storage systems, and AI rapidly made their way into our world and our homes, we learned to adapt and experiment though trial and error, as there was no previous generation to show us the ropes.

When I got my first computer and opened up my first social media profile at eight, to play online games with my school friends, none of us thought about the consequences of our actions. Starting to engage in these technologies with the purest of intentions, in our attempt to fulfill the basic human need to socialize, to connect, we made ourselves vulnerable to the dangers that lurked in the technologys shadows. The world has since realized its mistake in being unprepared against these dangers, but many of us had already become victims of catfishing, hacking, and even stalking.

While technology offered us many benefits there was always equal amounts of risk involved. Having learnt this in the past decade, both adults and children, it is our responsibility to provide guidance, protect the next generations and help navigate these technologies responsibly and for the good of all. It is our job to take action on minimizing the risks and maximizing the benefits, and provide the necessary wisdom and support, which we, the transition generation, lacked.

Kathleen Esfahany, Computer Science & Neurology, MIT, USA

I was born in 2000 to two computer scientists. Although technological innovation dramatically changed my life with each passing year, the shift into a technology-filled world felt entirely natural to me. The phenomenon of mirrored growth helped cement my identity as a digital native: for much of my childhood, each milestone in my own cognitive development was mirrored by technological advances and a deepening immersion in technology as an educational and social tool.

As my curiosity about the world grew, online news and social media proliferated, making it possible for me to follow events and connect with others across the globe. I can also thank my parents, who used their expertise on computers to help me understand the seemingly magical devices around me, empowering me to think about how to use technology for my own purposes and create technology of my own.

Todays youth are growing up with rapid advances in AI. Already, we are seeing how the unprecedented efficiency and personalization of AI-powered technology can elevate todays youths ability to learn, form personal relationships, and create joy. To optimize it for childrens safety and emotional well-being, I believe it is critical that AI-powered technology is designed so that it can match the diverse needs and abilities found throughout childhood development. My hope is that the combination of well designed AI-powered technology for youth and educational programs about AI will empower and inspire todays youth to harness AI responsibly to bring to life their visions for the future.

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AI ethics: digital natives on protecting future generations | World Economic Forum - World Economic Forum