Author: Faraz

  • Paedophiles using AI to turn Celebrities into kids…

    Paedophiles are using artificial intelligence (AI) to create images of Singer and Film Stars as children.

    Pic Credit By Joe Tidy Cyber correspondent.

    IWF stated that pictures of female celebrity remade to look like a child are distributed among pedophiles.

    Images of child actors are also being tampered with in one dark web forum, according to the said charity.

    Bespoke image generators can create hundreds of photos of actual victims of child sexual abuse.

    More recently, these kinds of details come from the IWF’s report on the increasing danger, and attempts to draw attention to this hazard.

    Ever since these powerful image generation systems became available in the public domain, researchers have cautioned that they could be used fraudulently to create explicit images.

    In May, Home Secretary Suella Braverman and US Homeland Security Secretary Alejandro Mayorkas released a joint statement warning against despicable AI-generated images that show adults engaging in sexual acts with minors.

    Predators are purportedly sharing images of a renowned female singer, imagined as a child.

    The organization asserts that in another dark web forum, pictures of the children actors are being edited to appear sexually.;

    Additionally, bespoke image generators are used to create hundreds of new photographs with actual subjects who are victims of child sexual abuse.

    It also gives the details gleaned from the IWF’s most recent report addressing the escalating predicament as a means of raising consciousness for the perils associated with pedophiles utilizing AI systems that convert simple commands into pictures.

    Once these powerful image generation systems became available to the public, researchers have cautioned about their abuse towards creating lewd pictures.

    May’s Home Secretary Suella Braverman and US Homeland Security Secretary Alejandro Mayorkas released a joint pledge to combat against “disgraceful artificial intelligence generated images of child sexual abuse perpetrated by paedophiles”.

    In its report, the IWF also outlines how more than 2,800 synthetic images that violate UK laws were identified by researchers who spent a whole month logging all artificial imagery on just one darknet site.

    According to analysts, a recent trend has seen predators taking just one photo of widely known young victims on child abuse scenes and using it as a basis for manufacturing multiple other such pictures in various sexually perverted environments.

    Into one folder were over 501 pictures of some little girl who was roughly 9-10 years old and sexually assaulted. Other’s were able to create additional images of “predators” by sharing a highly tuned AI model file into the same folder.

    Some of their imagery is so real; even adults would mistake it for real child pornography, e.g., images of even celebrities portrayed pretending to be children.

    They noticed photos primarily of younger looking female singers or actresses, which had been altered with the imaging software package to make them appear as kids.

    No names or specifics of those who were involved in the attack were mentioned in the report.

    The organization stated that it was publishing the study in order to have the problem placed on the agenda during the British AI Summit next week at Bletchley Park.

    Within one month, the IWF looked into 11,108 images generated by an artificial intelligence that were circulated in a darkweb child abuse forum.

    Out of this number, 2,978 were identified and certified that meet the definition of “images which fail the test of UK law”, meaning they showed child sexual abuse
    Out of the total 2857 pictures, more than twenty percent were rated as type A, which is the worst category.
    The majority of these photos comprised about 54% (1,372), which involved primary school-aged persons (seven to ten years).
    Besides this, there were 143 images displaying children between the ages of three and six years and two displaying babies (less than two year).

    The fears that the IWF gave in June are now the reality as predators are already taking advantage of AI to create obscene pictures involving children.

    This was stated by Susie Hargreaves,, the CEO of IWF,, our worst nightmares have happened.

    Three months ago, we said AI imagery may soon appear as genuine photographs depicting abuse of children, and that these images are going to appear in abundance. This is already a fact.

    AI-based images remain harmful as observed in the IWF report. The images may not actually harm children directly but they legitimize and normalize predators’ behavior while wasting police manpower searching for the non-existent children.

    New forms of offences are also being investigated in some cases resulting into new complexities for enforcing agencies.

    One case where this happened was when another organization called the IWF located several pictures of at least two girls who were depicted in Category A sexual abuse scene based on their manipulated photos from an innocent photoshoot at non-nude modelling agency.

    In truth, they are being prosecuted for category A offences that did not occur at all.

    The IWF has now stated that its fears of such concerns turning into a reality have become true in June.

    In this respect, as indicated by Susie Hargreaves, the CEO of IWF, “our worst nightmares have become reality”.

    “As warned by us earlier in the year, the day would come when artificial intelligence imagery would soon resemble authentic photos of children being sexually abused; a stage that we are currently past.”

    The IWF report states in essence that true-to-life harm is a reality when it comes to AI images. However, these materials do not harm children directly during their production. In addition, photos of fictional victims become a normal part of predatory behavior. It may also be that police have to spend time searching for children whom do not exist.

    In some cases even new ways of committing offense are studied, putting extra problems in the way of law enforcement agencies.

    As an illustration, the IWF identified lots of photos of two girls who were not even ten years old in which their unaltered images taken for a photoshoot at a non-nude modeling company had been altered to make them appear in category A sex offenses.

    What this means is that they’re actually victims of Category A offences that did not take place.

  • Censys lands new cash to grow its threat-detecting cybersecurity service….

    Image Credits: baranozdemir / Getty Images

    It appears that investments in cybersecurity companies are starting to come at the crossroads.

    Despite a tough summer, total venture capital investment to security start-ups grew from approximately $1.7 billion to almost $1.9 billion in Q3 according to Crunchbase, representing a small increase of 12%. That’s still down 30% annually. However, it’s just a mild indication that ventures hunger for cyber is once again on the rise.

    The reasons may include increasing expenditures on enterprise cyber security among other factors.

    As noted in an IANS Research brief, corporate IT security spending increased by about six percent from 2022 to 2023 – only marginally meaningful. Accordingly, most of the respondents pointed out to “increased risk” and “digital transformation.” That is, digitizing legacy apps, products and services.

    This helps one startup called Censys that offers insights on vital internet-connected infrastructure that are of interest to customers. Censys today revealed that it secured $50M in Series C financing and another $25M debt round, resulting in a cumulative figure of $178.1 million of funds raised.

    Censys traces back to ZMap invented by the organization’s chief scientist Durumeric in 2013. In 2015, after understanding that this program can be used for enhancing instead of monitoring the states of insecurity on the internet, Durumeric has mobilized some team to revamp and develop ZMap’s performance.

    According to Brad Brooks, the CEO of Censys, their belief remains unchanged as stated during an email interview at TechCrunch. Perfect visibility of any element that has a link with exposure to the internet is the only way through which vulnerable people and organisations can be protected against external threats.

    Thus, Censys has now a variety of mechanisms for tracking the hosts, as well as their respective security states or statuses. The customers enjoy database of vulnerabilities “enhanced by context”, as well as an environment for watching and assessing their openness-oriented assets (for example, servers and office computers).

    According to Brooks, most users have been leveraging the platform as a threat hunter tool where they hunt for and investigate potential security compromises, while also integrating the asset monitoring technology from Censys. Others use Censys to assess how effective their security measures have been so far in terms of length and intensity of risk.

    According to Brooks, “Censys utilizes leading internet intelligence to provide leaders and their teams with the data they need to stay ahead of sophisticated cyber-attacks.” They may also employ the benchmark of Census which are used in assessing a company performance over a period and creating industry standards.

    In recent times, Censys got into the generative AI wagon and created a chatbot that allows people to search their security databases using natural languages. For instance, they may query the platform by putting forth requests such as “show me all Australian locations with both FTP and HTTP”. For instance, they can use the chatbot to translate search syntax generated from a third party threat hunting platform such as Shodan and/or Zoomeye into search language for Censys can understand.

    mage Credits: baranozdemir / Getty Images

    Censys is a firm that claims to have 350,000 subscribers in its free service and above one hundred eighty (180) clients, including the FED and the department of homeland security, among others. This firm intends to use its As of now, it should be noted that by the end of 2023, Censys from Ann Arbor, MI should hire a hundred and fifty employees.

    According to Brooks, the demand for cybersecurity is still high even with economic uncertainty. In fact, there were positive winds due to major enterprises moving to cloud based solutions and relocating thousands of employees to working remotely that drove up demand and queries.

    It is not easy for Censys to sustain speed and maneuver over fierce headwinds.

    The remaining months of 2016 are threatened with a stagnant venture funding or perhaps even a decline due to issues in the Europe and the relation between US and China. The higher interest rates are reducing opportunities for new investment which then impacts startups ability to obtain money.

    Hedge funds, late stage and crossovers – investors into publicly traded and privately held corporations – have retreated from the market due to anticipated recession – in general, technology sector.

    However, on another note, there are grounds for hope.

    As noted in a recent Spiceworks report, although most businesses fear a recession in 2024, it does not mean that two-third (about 66%) of them do not plan to increase the annual budget and include allocations for cyber security in 202 Accordingly, according to IDC there will be cyber security spending of over $219 billion around the globe for this year, rising up to almost $300 billion by 2026 (IDC, 2020).

  • Twelve Labs is building models that can understand videos at a deep level through AI..

    Image Credits: boonchai wedmakawand / Getty Images

    One aspect of text generative AI. However, image-understanding AI models could open up a whole new set of opportunities.

    Take, for example, Twelve Labs. As co-founder and CEO Jae Lee states, the San Francisco-based startup teaches AI models to deal with complicated audio-video alignment issues.

    He said that “Twelve Labs was set up with a view to developing the necessary infrastructure for multi-modal video understanding; the initial undertaking being video semantic search – ‘CTRL+F for videos.” “Twelve’s vision is about building tools for creating intelligent software that can hear

    The purpose of Twelve Labs’ models is to be able to link some words in natural languages with what is occurring within a video like actions, objects as well as some background sound that has enabled developers to develop mobile applications which are able to search through videos as well as classification of scenes and extr

    According to Lee, Twelve Labs’ technology can be used in things such as inserting ads and content moderation, like distinguishing which knife video is violent or pedagogical. Lee further added that it could be used as media analytics, and auto-generated highlight reel — or blog post headline and tag from video.

    During my conversation with Lee, I enquired her whether there are any chances of bias in such models being modeled and this is because established scientific fact indicates that models exacerbate the biases within and upon which they were trained.o As an example, a video understanding model trained based on mostly clips from local news—that focuses a lot of attention on crime, using racialized and sensationalized language—may internalize its own lessons by picking up racism and sexism along the way.

    One example of it would be text-generating AI. However, next-generation computer vision systems will open up for a whole world of possibilities.

    Take, for example, Twelve Labs. According to Jae Lee, a co-founder and CEO of the San Francisco-based start-up, the company trains AI models to “crack hard video-language alignment puzzles”.

    As explained by Lee to TechCrunch via email,” Twelve labs was established…for constructing a multimodal video comprehension platform with the initial aim being semantic search — or control F for videos.” The mission is for experts to be able to develop programs whose senses are similar

    The twelve lab’s models, try to map out some natural language with what is going on in a video such as; movements, objects and sound effects surrounding, which allow developers to make applications capable of searching inside the videos, splitting the videos into chunks or chapters, summing up the videos,

    Twelve labs’ technology can help in activities such as adding ads or moderating violent knife videos. According to Lee, it may also be applied in media analytics and to create highlight reels/blog post headlines or tags based on videos.

    Thus, when I asked Lee whether there is a potential for any kind of bias in these models, it is worth noting that it is long-standing science practice and theory that models tend to enhance the pre-existing data biases. As a case in point, teaching a video cognition model primarily with clips of local news – that frequently devotes much time to the racialized and sensational reporting about crime – may lead the model to incorporate both gender bias and racial discrimination.

    According to Lee, Twelve Labs tries to adhere to internal biases and “fairness” for the models’ performance before their release will happen. He also asserts that they plan to publish model-ethics-related benchmarks and datasets at one point. However, apart from that point there was nothing else to talk about.“As to the difference between our product and the large language models such as Chat GPT, our model is specifically designed and constructed to make sense of all things in video – integrating visual signals, audio, speech elements which together give.

    MUM is another multimodal model that Google is working on, which its incorporating into video suggestions across Google Search as well as YouTube. In addition to MUM, Google – along with Microsoft and Amazon – provide API-based, AI-enabled functionality for object, place and action recognition in videos with frame-level extraction of rich metadata.

    However, Lee maintains that Twelve Labs is segmented by its superior models and the automaton function of the platform where users can refine the platforms generic model with their own data for more “domain-specific” video analysis.

    Mock-up of API for fine tuning the model to work better with salad-related content.

    Today Twelve Labs is launching its most recent “multimodal model”, called “Pegasus-1”; this model understands different types of prompts referring to full-video analysis. Pegasus-1 can also provide an in-depth description of a specific video or simply some highlights and timestamp.

    According to Lee “Most business use cases require understanding video material in much more complex ways than the limited and simplistic capabilities of conventional video AI models can provide. The only way enterprise organization can get a human level understanding without having to analyse video material in person is to leverage powerful multimodal.

    Lee claims that since launching in private beta back in early May, Twelve Labs currently has over 17k developers registered. Currently, the company is working together with an indeterminate amount of organizations in sectors such as sports, media, e-learning, entertainment and security. Moreover, recently the organization established partnership with NFL.

    Speaking of Twelve Labs, which is still raising money – this is a must-have component in any start-up. The firm said it has just closed a USD 10M strategic financing deal from Nvideo, Intell, an Samsung Next, taking its total up to USD 27M.

    According to Lee, this new investment targets strategic partners who can quicken the company in research (Compute), products and distribution. “According to research from our lab, it is a fuel for the continued video-understanding innovation which allows us to introduce the most potent models to end users regardless of potential uses. We push the industry boundaries at a time when enterprises are doing amazing stuff,” it says.

  • Apple’s job listings suggest it plans to infuse AI(Artificial Intelligence) in multiple products..

    Apple seems to be finally getting serious about infusing generative AI into its products — both internal and external — after announcing a solitary “Transformer” model-based autocorrect feature on iOS 17 earlier this year.

    Nevertheless, an idea emerges that Apple seems to be getting serious about generative AI integration within its devices as part of and without iOS 17 after launching of a single iOS version 17 with autocorrect feature based on a Transformer model earlier this year.

    In fact, the organization has issued jobs on a daily basis in the recent days due to its concerns about generalized artificial intelligence. For instance, in its statement of a role on the Apple store platform it states that the company is developing a generative AI based developer service tool for internal use which will assist its app development team.

    Image Credits: Haje Kamps / MidJourney

    A conversational AI platform (voice and chat) job task that will relate with customers in Apple Retail is stated in the job advertisement. Apple’s job advertisements include task of text-generation, long-form; summarization; question answering.

    Other companies also work on build core models and give this way a human-like conversational agent as the example of core build models. Once again, the firm has posted vacancies at Siri Information Intel unit, which covers among others Siri Search widget and so on. In addition, apple aims at finding partner from among developing localised models for device-based operation developers.

    However, this time round, Apple has been a lot clear in giving their word on who they want.

    According to the Bloomberg news, Apple will be spending over one billion dollars annually on advertising services and AI supported devices. “Really a pretty big miss”, says a source close to it to the Wall Street Journal.

    Still, there seem signs of seriousness in taking generative AI to the company’s internal product and external services, for example, after singular Transformer mode-based auto correction tool introduced in iOS 17 back on the start of a new year.

    In fact, this company posts job openings almost every week saying that for some reason generative AI needs people. For instance, in its statement of a role on the App Store platform, the organization states that they are currently developing a generative AI-based developer experience platform for internal use and that this will assist its app developers.

    Amongst other roles a conversational AI platform (voice and chat) Apple Retail should interact customers as stated in the job posting. Apple job listings refer to long-form text-generation as a task, summarization, and question answering.

    Other jobs in AI/ML consider they are creating fundamental designs and offer as an example of developing fundamental designs a “human-like conversational agent”. Again, the company has advertised for vacancies in fields including Siri informational intelligence that involves Siri and search widgets amongst others. Moreover, apple wants to partner with these builders building a mode of operation on device based localised model.

    Unlike in their previous advertising campaigns where they were not this explicit when listing people whom they wanted to reach out to, this time around, Apple has been crystal clear of who they wish to cater to.

    As per Bloomberg News, Apple is also planning to spend over a billion dollar annually in advertisement for promoting its AI products or services. As quoted by Wall Street Journal, a source close to it said that, “it’s quite a big miss not to ship generative AI products”.

    According to Bloomberg, the company intends to utilize LLMs to drive features such as sentence completion on the latest version of iOS targeted at powering Siri and Messages application. The report revealed that Apple is looking into incorporating generative AI -powered enhancements in apps and services like Xcode to assist developers as seen by the job posting above possibly. Additionally, it revealed AI-generated playlists for Apple Music, and AI -assisted writing for Pages and Ke

    Several news stories indicate that Apple is working on an internal project dubbed “Apple GPT.” Nonetheless, except in its operating system, very little has been released for end users so far. In contrast however, their competitors like meta, Google, and Microsoft are integrating AI across several hardware.

    As for the coming versions of iOS, according to Bloomberg, it is reported that the goal of the company is to draw upon large language models (LLMs) to empower such functions as sentence completion for the company’s virtual assistants such as Siri or the messages app. On this regard, the report noted that apple is developing generative ai assisted features in applications and services such as xcode which helps developer (this position above could be one of them) , ai generated music tracks on Apple music, and ai assisted writing for pages and keynote.

    Reports indicate that Apple may develop ‘Apple GPT’, and as of now, no such consumer product has been developed in this regard. However, Cupertino-based rivals of Apple like Google, Microsoft, and Meta have included their AI-powered features in different types of software and

  • Google Pixel’s face-altering photo tool sparks AI manipulation debate….

    Google Pixel’s face-altering photo tool sparks AI manipulation debate….

    The camera never lies...

    It’s never easy to get everyone looking just right when taking a group photograph.(BBC News)

    what it means to photograph reality.

    Google’s latest smartphones released last week, the Pixel 8 and Pixel 8 Pro, go a step further than devices from other companies. They are using AI to help alter people’s expressions in photographs.

    It’s an experience we’ve all had: one person in a group shot looks away from the camera or fails to smile. Google’s phones can now look through your photos to mix and match from past expressions, using machine learning to put a smile from a different photo of them into the picture. Google calls it Best Take.

    Further, they allow a user to do away with a person or building among others in the picture, filling in the gaps using an element referred to as Magic Editor. This is called deep learning which is a type of artificial intelligence technique, whereby the algorithm predicts how the texture will be within gaps seen among the pixels based on the knowledge that it obtains from millions of other images.

    Not necessarily are those photographs shot on the phone only. Applying the magic editor or best take on any of photos in your google photo library using your pixel 8 pro.

    It is essential to recall that the application of AI in smartphone photography does not intend to produce photos similar to reality as claimed by professor Rafal Mantiuk, a specialist on graphics and displays at the University of Cambridge.

    He says, “People do not wish to record reality.” Pictures look nice on their screens, but are not accurate representations of reality. That’s what it is all about for them.

    Machine-based learning supplements missing data from smartphone’s photos because these gadgets possess certain physical limits.

    It assists one to increase zoom, capture pictures at night, and in the case of Google’s magic editor mode, it adds additional objects into the photo that can be replaced by others.

    Prof. Rafal Mantiuk, who is a specialist on graphics and display at University Cambridge said we should always acknowledge that it is not about making artificial photos appear as if they are true images.

    ”People do not want to record reality as it is”, he remarked. They are going for beautiful photos, and all smartphone photo pipe should make photographs look good — not realistic.

    Smartphones use machine learning to supplement missing pieces of data in a photo, because of their limited physical capabilities.

    This enhances their capacity for zooming, capturing in low-light settings and in magic editor mode where even Google swaps a smile for a frown.

    Altering images should never surprise anyone because it started way back in the field. However, nothing else has ever been more feasible than enhancing actuality with the help of AI.

    Several months ago, Samsung found itself at the centre of a storm over the techniques it employed for enhancing lunar pictorial effects via Deep Learning models._: No matter how poor images taken, it provided usable photos.

    So what I mean is – your Moon picture was not necessarily a picture of the Moon that you were looking at.

    The company recognized the criticism and stated they were trying “to lessen any possible confusion which can arise between capturing the real moon on camera and an image of the moon”.

    Changing images is not a novelty – it’s as old as art. However, has it ever been easier for the fake to augment the real through the use of artificial intelligence?

    In particular, earlier this year, Samsung was criticized for its use of deep learning algorithms to enhance photos of the Moon taken on its smartphones. You would get usable images no matter how bad of an image, to start within the tests they did.

    Put simply – that wasn’t necessarily the Moon you were looking at in your Moon photo.

    One statement by the company was as follows; “the company is striving to cut out any possible mix-ups between snapping a live moon or screen shots”.

    According to Reynolds, Google includes metadata with their pictures by attaching a digital trail about an image or tagging them if artificial intelligence was employed.

    This is something we will discuss within our team. And we’ve talked at length. Since we’ve been working on these things for years. He says, “It’s something that we have, an exchange with our users, listening to what they say.”

    The fact that the AI-based features are central in Google’s marketing advertisement proves that it is confident the consumers will agree.

    Then, where does Google draw the line in terms of image manipulation?

    According to Mr Reynold, the discussion surrounding the use of artificial intelligence was too elaborate for one to draw a demarcation line that went beyond the boundary limit.

    He states, “As you delve further in developing features one appreciates that a line is more or less of an overstating of the most complicated one-up of feature by feature choice.”

    In addition, these new technologies increase ethical debate over what is and what isn’t reality.” However, we still have limited vision through our own eyes.”

    He said: We are able to notice crisp color images since the brain has the ability to compute information and make inferences.

    Therefore, if people argue that the camera does “false things”, they should be aware that the human brain does such things in a different manner.

    Reynolds claims on Google’s new tech that the firm applies metadata to their images which are an indication of how to identify AI in a photo by means of an industry standard.

    This is a sensitive question we speak of internally. And we’ve talked at length. Therefore, we have been busy with them for many years. He says, “It’s a discussion; we listen to each of our users.”

    It is obvious that Google has complete confidence in the users’ consent and it uses the AI-centered advertisement campaign for its new phones.

    Therefore, where does Google drawn the line in image editing?

    A statement by Mr Reynolds would be that the debate on whether or not to use artificial intelligence is too complicated for one to just draw a line and say this is too far.|ilton, A. J., Gargano, M. A., & DeSario, R. A.

    He explains that it dawns on him as he delves deeper into building features that a line is actually over simplifying what turns out to be a very complicated “feature by feature” decision.

    Even as these new technologies bring up questions on what it really means by what’s real and what’s isn’t, Professor Mantiuk suggests that in consideration of the same issues, even our eyes may not always show everything.

    He said: We see distinct and bright images since our brain can regenerate data and deduce or infer even what seems absent.

    “Therefore, you can bitch-slap camera for “faking things”, whereas the human brain is not much different.”

    (Getty Images)

  • The Dark Side of AI: Understanding the Potential Dangers…

    Artificial Intelligence (AI) has made significant advancements in recent years, transforming various industries and revolutionizing the way we live and work. From virtual assistants to autonomous vehicles, AI has undoubtedly improved efficiency and convenience in many aspects of our lives. However, amidst the remarkable progress, we must also analyze and understand the potential dangers that AI poses. In this article, we will explore some of the ethical concerns, privacy risks, job displacement, and issues of bias and discrimination associated with AI technology.

    As AI becomes increasingly capable of making independent decisions, ethical concerns arise regarding the use and deployment of such technology. One major concern is the potential for AI to be weaponized or used for malicious purposes. For example, autonomous drones equipped with AI algorithms could be used to carry out targeted attacks or surveillance, raising concerns about the lack of human control and accountability.

    Additionally, AI algorithms can amplify existing social biases, leading to unethical decisions and actions. Biased training data or biased algorithm designs can result in discriminatory outcomes, particularly in areas such as criminal justice, hiring processes, and financial lending. The responsibility lies with developers and policymakers to ensure fairness and transparency in AI systems.

    The widespread adoption of AI often involves collecting vast amounts of personal data. This data can range from personal preferences and browsing habits to highly sensitive information. Privacy risks arise when this data is exploited or mishandled.

    AI algorithms rely on massive datasets to make accurate predictions or decisions. While data anonymization techniques are typically employed, there is always a potential risk of re-identification. If an individual’s identity can be linked to their data, it can be used for targeted advertising, manipulation, or even identity theft. The increasing integration of AI into everyday devices and services further amplifies the potential privacy risks, emphasizing the need for robust data protection and privacy regulations.

    One of the major concerns regarding AI is its impact on the labor market. As AI technologies continue to advance, there is a fear that automation will lead to significant job displacement across various industries.

    Tasks that were once performed by humans can now increasingly be handled by AI-powered systems. Jobs in sectors such as manufacturing, transportation, and customer service are already witnessing the effects of automation. While new jobs may be created in AI development and maintenance, the transition period can be challenging for those whose jobs are at risk of being replaced.

    Efforts to address job displacement involve upskilling and reskilling programs to equip individuals with the skills needed for the jobs of the future. Furthermore, policymakers must consider measures such as universal basic income to support individuals who may find it difficult to adapt to the changing job landscape.

    AI algorithms are trained on historical data, which often reflects the biases and prejudices present in society. Consequently, AI systems can perpetuate and even amplify these biases, leading to discriminatory outcomes.

    One prominent example is the use of AI in predictive policing. If historical crime data is biased against certain demographic groups, the AI system will learn and reinforce these biases, leading to racial profiling and unjust outcomes. Similarly, biased algorithms in hiring processes can result in discrimination against certain individuals or groups.

    Addressing bias and discrimination in AI systems requires careful attention to the data used for training and the design of the algorithms. Regular audits, diversity in AI development teams, and input from affected communities are crucial in mitigating these issues and ensuring fairness in decision-making processes.

    Conclusion:-

    While AI has immense potential for positive impact, it is essential to acknowledge and address the potential risks and dangers it brings. Ethical concerns, privacy risks, job displacement, and issues of bias and discrimination are key areas that require continual evaluation and regulation to ensure the responsible development and deployment of AI systems. By understanding and actively working to mitigate these risks, we can harness the benefits of AI while safeguarding against its darker implications.

  • What Is a Blockchain?

    What Is a Blockchain?

    A blockchain represents a distributed database or ledger that is shared among the nodes within a computer network. Its most prominent application is in the realm of cryptocurrency, where it plays a critical role in maintaining a secure and decentralized record of transactions. However, its utility extends far beyond the realm of digital currency. Blockchains have the capability to render data immutable, meaning it cannot be altered, across a wide range of industries.

    The primary assurance required within a blockchain system is at the point where a user or program inputs data. This inherent feature reduces the reliance on trusted third parties, typically individuals like auditors, who can introduce costs and errors into the process.

    Since the advent of Bitcoin in 2009, the utility of blockchain technology has witnessed a rapid expansion. It has found application in various cryptocurrencies, decentralized finance (DeFi) applications, non-fungible tokens (NFTs), and smart contracts, marking a significant evolution in its use cases.

    [Investopedia / Xiaojie Liu]

    How Does a Blockchain Work?

    You may have some familiarity with spreadsheets or traditional databases. In essence, a blockchain shares a similarity with these systems, as it serves as a database for the entry and storage of information. However, the key distinction between a conventional database or spreadsheet and a blockchain lies in how data is structured and accessed.

    A blockchain comprises of programs known as scripts, performing tasks akin to those in a typical database: inputting and retrieving information, as well as saving and storing it. The unique attribute of a blockchain is its distribution, where multiple copies are stored on numerous machines, and they must all match to validate the data.

    Within the blockchain, transaction details are collected and recorded within a block, analogous to a cell in a spreadsheet that holds information. When a block reaches capacity, the data undergoes encryption through an algorithm, resulting in the creation of a hexadecimal number known as the “hash.”

    This hash is subsequently integrated into the header of the next block, combining with other information within the block and forming a connected series of blocks, thereby creating a blockchain.

  • Digital Revolution: Technology, Power, & You

    Digital Revolution: Technology, Power, & You

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    The digital revolution has transformed the way we go about our daily lives, how we earn a living, and how we communicate with one another. And it’s just the beginning of this remarkable journey. However, while these technologies have the potential to enhance the well-being and productivity of billions of individuals, they also bring forth new hurdles for people and governments across the globe. Recent incidents, ranging from interference in elections to data breaches and cyberattacks, have underscored the profound ways in which technology is reshaping our notions of privacy, national security, and even the very foundations of democracy.

    In this project, we delve into challenges across five critical domains that will define the future of the digital era: the justice system, its impact on democracy, global security and international conflicts, the effects of automation and artificial intelligence on the job market, as well as issues surrounding identity and privacy. We invite you to explore thought-provoking topics that shed light on how technology is influencing our lives.

  • Artificial intelligence (AI)

    What is Artificial Intelligence (AI)?

    Artificial intelligence encompasses the emulation of human intelligence processes by machines, particularly computer systems. AI finds diverse applications, including expert systems, natural language processing, speech recognition, and machine vision.