We're past the point where utilizing the latest technology is confined to big businesses with budgets to spare. The pandemic with its accompanying mandated lockdowns and changes in the demands and requirements of customers and markets are forcing companies to adapt to digital transformation. Put bluntly, those who want to remain in business have to keep up with the latest digital transformation trends. Some of them might already be familiar because they belong to innovations that have been in development for a long time. However, most will soon be ready for application and will be taking center stage in 2021. So we will be detailing each one to help you understand how these trends can affect your business in the coming years. With innovations being developed left and right, technological evolution comes into play. New technologies radically transform our lives into something that seemed unthinkable in the old times. However, it's not just our personal lives that are being altered by modernization. Businesses are also grabbing what they can when it comes to technological advancement. With the onset of the pandemic, they have to leverage new technologies to remain relevant in the digital age.

The human brain is the body's very own supercomputer. It uses approximately 100 billion neural connections and is capable of processing 11 million bits of information each second. Neurotechnology is a rapidly developing field of technology that is attempting to harness that powerful and complex brain power. With that growth in "mind control" comes a narrowing of the border between humans and machines. Neurotechnology refers to any technology that provides greater insight into, or control over, the activity of the brain or nervous system.

The testimony to the advancement of science would soon be witnessed as a robot is going to represent a client in the court. The defendant, in a first, will use legal assistance provided by Artificial Intelligence, as per reports. The one-of-a-kind robot has been developed by a firm "DoNotPay". It will be in the form of an application in the smartphones, which will instruct defendant on what he needs to say before court, just like any lawyer would do to its clients. The peculiar technology has been introduced by Joshua Browder, who is founder and the CEO of firm DoNotPay.

AI alignment considers how we can encode AI systems in a way that is compatible with human values. The normative side of this problem asks what moral values or principles, if any, we should encode in AI. To this end, we present a framework to consider the question at four levels: Individual, Organizational, National, and Global. We aim to illustrate how AI alignment is made up of value alignment problems at each of these levels, where values at each level affect the others and effects can flow in either direction. We outline key questions and considerations of each level and demonstrate an application of this framework to the topic of AI content moderation.

We present PatentsView-Evaluation, a Python package that enables researchers to evaluate the performance of inventor name disambiguation systems such as PatentsView.org. The package includes benchmark datasets and evaluation tools, and aims to advance research on inventor name disambiguation by providing access to high-quality evaluation data and improving evaluation standards.

This paper aims to investigate the possibility of using Artificial Intelligence (AI) to resolve the legal issues raised by the Covid-19 emergency about the fate of contracts with continuous, repeated or deferred performance, as well as, more generally, to deal with exceptional events and contingencies. We first study whether the Italian legal system allows for'maintenance' remedies to face contingencies and to avoid the termination of the (duration) contracts while ensuring effective protection of the interests of both parties. We then give a complete and technical description of an AI-based predictive framework, aimed at assisting both the Magistrate (during the trial) and the parties themselves (in out-of-court proceedings) in the redetermination of the rent of commercial lease contracts. This paper aims to investigate the possibility of using Artificial Intelligence (AI) to resolve the legal issues raised by the Covid-19 emergency about the fate of contracts with continuous, repeated, or deferred performance, as well as, more generally, to deal with exceptional events and contingencies. However - even if the predictive system was initially intended to deal with the very specific problem connected to Covid-19 - the knowledge acquired, the model produced and the research outcomes can be easily transferred to other civil issues (see Section 5). In particular, jurists had to deal, on the one hand, with the distribution of the contractual risk and, on the other hand, with the management of contingencies from a perspective of preservation of the contract. The Italian Civil Code provides remedies to face both contingencies and breach of the contract. As for the first profile, the attention must be first of all directed on the Termination for Supervening Impossibility (art. Similar'demolition' remedies are a consequence of the non-fulfillment of the obligation: in this case, the attention must be first focused on the debtor's liability (art. These remedies allow only the disadvantaged party to cancel the contractual relationship. However, the termination of the contract does not always respond effectively to the interests pursued by the parties, who may prefer to continue the contractual relationship, even if under amended provisions.

Text-to-image generation models that generate images based on prompt descriptions have attracted an increasing amount of attention during the past few months. Despite their encouraging performance, these models raise concerns about the misuse of their generated fake images. To tackle this problem, we pioneer a systematic study on the detection and attribution of fake images generated by text-to-image generation models. Concretely, we first build a machine learning classifier to detect the fake images generated by various text-to-image generation models. We then attribute these fake images to their source models, such that model owners can be held responsible for their models' misuse. We further investigate how prompts that generate fake images affect detection and attribution. We conduct extensive experiments on four popular text-to-image generation models, including DALL$\cdot$E 2, Stable Diffusion, GLIDE, and Latent Diffusion, and two benchmark prompt-image datasets. Empirical results show that (1) fake images generated by various models can be distinguished from real ones, as there exists a common artifact shared by fake images from different models; (2) fake images can be effectively attributed to their source models, as different models leave unique fingerprints in their generated images; (3) prompts with the ``person'' topic or a length between 25 and 75 enable models to generate fake images with higher authenticity. All findings contribute to the community's insight into the threats caused by text-to-image generation models. We appeal to the community's consideration of the counterpart solutions, like ours, against the rapidly-evolving fake image generation.

An AI-based legal advisor is set to play the role of a lawyer in an actual court case for the first time. Via an earpiece, the artificial intelligence will coach a courtroom defendant on what to say to get out of the associated fines and consequences of a speeding charge, AI-company DoNotPay has claimed in a report initially from New Scientist and confirmed by Gizmodo. The in-person speeding ticket hearing is scheduled to take place in a U.S. courtroom (specifically, not California) sometime in February, DoNotPay's founder and CEO Joshua Browder told Gizmodo in a phone call. However, Browder and the company wouldn't provide any further case details to protect the defendant's privacy. DoNotPay is also reticent to disclose case specifics because what they're doing is likely in violation of courtroom laws and protocol.

Despite a global rush toward enterprise digital transformation, the document remains at the heart of most businesses, and unfortunately, managing them still remains a distinctly manual process. Despite its structured nature, the flexibility of a document makes it hard to automate business processes, and taking data from multiple line-of-business applications to insert it in a document is a matter of cut-and-paste, from screen to document and often back again once a document is received. Launched at Ignite in October 2022, Microsoft Syntex is here to solve some of these tediously manual issues, adding document processing tools to SharePoint. The solution uses machine learning to help construct and parse documents, turning a manual process into one where humans guide and check software, and where legal, regulatory and contractual requirements are still met. In this in-depth look at Syntex, learn more about content AI and some of the current use cases for this release.

How did we get to where we are today in the field of generative AI? Generative AI will be the most disruptive technological innovation since the advent of the personal computer and the inception of the Internet with the potential to create 10s of millions of new jobs, permanently alter the way we work, fuel the creator economy, and displace or augment 100s of millions of workers in roles from computer programmers to computer graphics artists, photographers, video editors, digital marketers and yes, even journalists. Even with all the hype around generative AI this year, it's true power has not yet been seen or felt, in 2023 there will be significant innovations that will begin a revolution that will leave no industry or job function un-impacted in one way or another. Although Generative AI has been a focused area of AI research since 2014, it really took off in the latter half of 2022 when the technology was put into the hands of consumers with the release of several text-to-image model services like MidJourney, Dall-E 2, Imagen, and the open-source release of Stability AI's Stable Diffusion. This was quickly followed up by OpenAI's ChatGPT which mesmerized consumers with a version of GPT-3 re-trained on conversational dialog that seemingly had an answer for everything and delivered responses in a very human-like manner. At the same time VCs looking for the hot new technology to invest in caught the generative AI bug and both Stability AI and Jasper both became instant unicorns with Series A funding exceeding $100 million.