This article is part of a VB special issue. Read the full series here: The metaverse - How close are we? Defining the "metaverse" is a difficult task, but one commonly accepted definition is a digital space populated by representations of people, places, and things. Through a combination of technologies including virtual reality (VR), augmented reality (AR), and AI, the metaverse that some futurists envision is an extension of the real world -- albeit without the physical trappings. Companies like Rockstar and Roblox have pitched the metaverse as the ideal platform for gaming, but there's no limit to the potential applications in the enterprise.

Art NFTs, in particular, made a big impact last year with Christie's reporting over $93 million in nonfungible token sales during its fourth annual Art Tech Summit that took place this past August. While notable, much of the crypto art scene appears to be dominated by cartoons and memes, as projects like CryptoPunks and Bored Ape Yacht Club have taken center stage. Known as "AI-generative NFTs," these nonfungible tokens are becoming increasingly popular within the art community, along with those interested in emerging technologies like artificial intelligence, blockchain and the Metaverse. Being able to work with an AI to bring your ideas to life is an experience like no other, it augments creativity in a way that feels like freedom, a type of play you haven't experienced since you were a child." While Eponym lets users create their own art NFTs, Metascapes is another project that was developed by three photographers looking to combine human expression with computer algorithms. While the potential for AI-generative NFTs is apparent, the question of whether or not artificial intelligence can be trusted to generate quality images based on text or photographs remains a concern. For instance, Fisher mentioned that Eponym has two versions of its generator available to the public, one on the company's Discord channel operating as a chatbot and the other as a private link that contains more complex algorithms capable of creating more advanced images. For example, Fisher remarked that Eponym's next project will feature interactive virtual identities where users can take their own portraits to create 3D avatars and animate them using artificial intelligence. Art NFTs, in particular, made a big impact last year with Christie's reporting over $93 million in nonfungible token sales during its fourth annual Art Tech Summit that took place this past August. While notable, much of the crypto art scene appears to be dominated by cartoons and memes, as projects like CryptoPunks and Bored Ape Yacht Club have taken center stage. Known as "AI-generative NFTs," these nonfungible tokens are becoming increasingly popular within the art community, along with those interested in emerging technologies like artificial intelligence, blockchain and the Metaverse. Being able to work with an AI to bring your ideas to life is an experience like no other, it augments creativity in a way that feels like freedom, a type of play you haven't experienced since you were a child."

If you are looking for what's next in ML, here are a few recent trends in machine learning research & industry. Machine Learning has become quite popular and everyone in tech is aware of it by now. It is even being adopted into the industry at a fast pace in different fields. To put it into perspective, here are some numbers from a research article. The global machine learning market is projected to grow from $15.50 billion in 2021 to $152.24 billion in 2028 at a CAGR of 38.6% in the forecast period.

Pimloc, a UK computer vision startup that's sharpened its business pitch to sell an AI service for quickly anonymizing video -- automating the blurring of faces or licence plates, along with a suite of other visual search services -- has grabbed another chunk of seed funding: Announcing a raise of $7.5M, led by Zetta Venture Partners, with participation from existing investors Amadeus Capital Partners and Speedinvest. The startup raised a $1.8M seed, back in October 2020, but says the new funds will be used to scale the business across Europe and the U.S., tracking the spread of data legislation and the evolution of public opinion around the privacy risks of biometrics -- pointing, for example, to the privacy backlash around Clearview AI. As well as building out its sales, marketing and R&D teams, Pimloc says the funding will be used to expand its product roadmap with a focus on video privacy and compliance. The business need it's targeting focuses on growing use of visual AI in industries like retail, warehousing and industrial factory settings -- for use cases like safety and efficiency. However the rise of AI-powered workplace surveillance tools create privacy risks for workers which could create legal and reputational risks for companies that deploy remote biometrics.

A computer scientist who said she was pushed out of her job at Google in December 2020 has marked the one-year anniversary of her ouster with a new research institute aiming to support the creation of ethical artificial intelligence. Timnit Gebru, a known advocate for diversity in AI, announced the launch of the Distributed Artificial Intelligence Research Institute, or DAIR. Its website describes it as "a space for independent, community-rooted AI research free from Big Tech's pervasive influence." Part of how Gebru imagines creating such research is by moving away from the Silicon Valley ethos of "move fast and break things" -- which was Facebook's internal motto, coined by Mark Zuckerberg, until 2014 -- to instead take a more deliberate approach to creating new technologies that serve marginalized communities. That includes recognizing and mitigating technologies' potentials for harm from the beginning of their creation process, rather than after they've already caused damage to those communities, Gebru told NBC News.

At Databricks, we have had the opportunity to help thousands of organizations modernize their data architectures to be cloud-first and extract value from their data at scale with analytics and AI. Over the past few years, we've been fortunate to engage directly with customers across industries and regions about their data-driven aspirations – and the roadblocks that slow down their ability to get there. While challenges vary greatly among industries and even individual organizations, we have developed a rich understanding of the top four habits of data and AI-driven organizations. Before diving into the habits, let's take a quick look at how organizations have approached enabling data strategies. First, data teams have made technology decisions over time that propel a way of thinking that is based around technology stacks: data warehousing, data engineering, streaming real-time data science, and machine learning.

Don't miss our upcoming virtual workshop with John Snow Labs, Improve Drug Safety with NLP, to learn more about our joint NLP solution accelerator for adverse drug event detection. The World Health Organization defines pharmacovigilance as "the science and activities relating to the detection, assessment, understanding and prevention of adverse effects or any other medicine/vaccine-related problem." While all medicines and vaccines undergo rigorous testing for safety and efficacy in clinical trials, certain side effects may only emerge once these products are used by a larger and more diverse patient population, including people with other concurrent diseases. To support ongoing drug safety, biopharmaceutical manufacturers must report adverse drug events (ADEs) to regulatory agencies, such as the US Food and Drug Administration (FDA) in the United States and the European Medicines Agency (EMA) in the EU. Adverse drug reactions or events are medical problems that occur during treatment with a drug or therapy.

There is enormous interest in and momentum around using AI to reduce the need for human monitoring while improving enterprise security. Machine learning and other techniques are used for behavioral threat analytics, anomaly detection and reducing false-positive alerts. At the same time, private and nation-state cybercriminals are applying AI to the other side of the security coin. Artificial intelligence is used to find vulnerabilities, shape exploits and conduct targeted attacks. How does an enterprise protect the tools it is building and secure those it is running during the production process?

Whether you've used social media, a navigation app or a picture filter, chances are that Artificial Intelligence (AI) has impacted you. It's not just you — AI is impacting human rights worldwide, and this course will inform and educate you on how your rights are affected by AI, and how you can be empowered to guard these rights. UNESCO and UNITAR jointly launched a new, short online learning course on AI and Human Rights for youths aged 16 to 24. Experts break down complex concepts about AI into straight forward activities built around our daily technology interactions. The course focuses on how freedom of expression, right to privacy and the right to equality are impacted using AI.

The current attention trade shows, media outlets, and (more and more) vendors are devoting to quantum computing is far from transitory. This form of computing is almost certain to play an integral part in the most meaningful future developments of Artificial Intelligence--if not in those for today. The bifurcation of quantum computing's applicability to AI is clear. On the one hand, "Quantum computing is necessary to reach Artificial General Intelligence," denoted Kyndi CEO Ryan Welsh. On the other, quantum computing is able to solve a critical problem related to AI that is a vital steppingstone to actually achieving Artificial General Intelligence. According to Welsh, quantum computing methods have a definite capacity for "fusing the gap between continuous mathematics and discreet mathematics," which is at the crux of the dichotomy between statistical AI and symbolic AI for Natural Language Processing applications.