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The earliest form of writing originated nearly 5000 years ago in Mesopotamia (present-day Iraq), representing the Sumerian language. However, these early manuscripts, inscriptions, manuals have suffered the wrath of time. Historians have long worried about the missing texts that could give an insight into the life and culture of ancient civilisation, Artificial Intelligence has now come to their aid.

Named after the Greek island in Homers Odyssey, Ithaca, the first deep neural network will help in not only restoring the missing text of damaged inscriptions, but also identifying their original location, and establishing the date they were written. Designed to assist and expand the historians workflow, this AI has achieved 62 per cent accuracy when restoring damaged texts and improved the accuracy of historians from 25 per cent to 72 per cent.

In a study published in the journal Nature, researchers said that models such as Ithaca can unlock the cooperative potential between artificial intelligence and historians, transformationally impacting the way that we study and write about one of the most important periods in human history.

Inspired by biological neural networks, deep neural networks can discover and harness intricate statistical patterns in vast quantities of data. Ithaca is one such development that merges the fields of technology, supercomputing, and ancient history to reveal unknown secrets hidden in plain sight.

Ithaca was trained to simultaneously perform the tasks of textual restoration, geographical attribution, and chronological attribution. Researchers trained the system on inscriptions written in the ancient Greek language and across the ancient Mediterranean world between the seventh century BC and the fifth century AD.

Credit: Ca' Foscari University of Venice

The architecture of Ithaca was carefully tailored to each of the three epigraphic tasks, meaningfully handling long-term context information and producing interpretable outputs to enhance the potential for human-machine cooperation. We believe machine learning could support historians to expand and deepen our understanding of ancient history, just as microscopes and telescopes have extended the realm of science Yannis Assael, Staff Research Scientist at DeepMind said in a statement.

Researchers said that as centuries went by, many ancient inscriptions were damaged and became partially or completely illegible. In some cases, they were removed from their original location, and they can be difficult to date. For instance, 2500 years ago, Greeks started writing on stone, ceramics, and metal, in order to register all sorts of transactions, laws, calendars, and oracles. Today, these archaeological findings reveal a lot of information on the Mediterranean area. Unfortunately, this tale is incomplete.

DeepMind has partnered with Google Cloud and Google Arts & Culture to launch a free interactive version of Ithaca. (File Pic)

Historians have already used Ithaca to shed light on current disputes in Greek history, including the dating of a series of important Athenian decrees thought to have been written before 446/445 BCE. Ithacas average predicted date for the decrees is 421 BCE, aligning with the new evidence and demonstrating how machine learning might contribute to historical debates.

Although it might seem like a small difference, this date shift has significant implications for our understanding of the political history of Classical Athens. We hope that models like Ithaca can unlock the cooperative potential between AI and the humanities, transformationally impacting the way we study and write about some of the most significant periods in human history, Thea Sommerschield, Marie Curie Fellow at Ca' Foscari University of Venice and fellow at Harvard Universitys CHS said.

Historians are now working on other versions of the AI, which has been trained in different ancient languages to study other ancient writing systems, from Akkadian to Demotic and Hebrew to Mayan.

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Meet Ithaca, Artificial Intelligence that will reveal hidden secrets of ancient civilisations - India Today

The JCO is an international, peer-reviewed medical journal published by the American Society of Clinical Oncology (ASCO), with an impact factor (IF) of 44.54. This is the first time that research on AI biomarkers has been published in an international SCI-grade journal of JCO's prestige.

"Immune phenotyping of tumor microenvironment is a logical biomarker for immunotherapy, but objective measurement of such would be extremely challenging," said Professor Tony Mok from the Chinese University of Hong Kong, co-senior author of the journal. "This is the first study that adopted AI technology to define the tumor immune phenotype, and to demonstrate its ability in predicting treatment outcomes of anti-PD-L1 therapy in two large cohorts of patients with advanced non-small cell lung cancer."

Immune checkpoint inhibitors (ICI) are a standard therapy method for advanced NSCLC with programmed death ligand-1 (PD-L1) expression. However, outcomes vary depending on the patient's tumor microenvironment.

Assessing the PD-L1 tumor proportion score (TPS) can bring predictive benefit for patients with high expression (over 50%), who show superior response to ICI therapy over standard chemotherapy. However, ICIs lose their potency in patients with PD-L1 TPS between 1% and 49%, showing outcomes similar to chemotherapy. Therefore, the development of an accuracy-enhanced biomarker to predict ICI response in NSCLC patients with low PD-L1 expression is highly warranted.

While tumor infiltrating lymphocytes (TIL) are promising biomarkers for predicting ICI treatment outcomes apart from PD-L1, clinical application remains challenging as TIL quantification involves a manual evaluation process bound to practical limitations of interobserver bias and intensive labor. Employing AI's superhuman computational capabilities should open new possibilities for the objective quantification of TIL.

To validate immune phenotyping as a complementary biomarker in NSCLC, researchers divided 518 NSCLC patients into three groups based on their tumor microenvironment: inflamed, immune-excluded, and immune-desert. As a result, clinical characteristics based on each immune phenotype group showed statistically significant differences in progression-free survival (PFS) and overall survival (OS).

Furthermore, analysis of NSCLC patients with PD-L1 TPS between 1% and 49% based on their immune phenotype found that the inflamed group showed significantly higher results in objective response rate (ORR) and progression-free survival (PFS), compared to the non-inflamed groups. This shows Lunit SCOPE IO's ability to supplement PD-L1 TPS as a biomarker by accurately predicting immunotherapy response for patients with low PD-L1 TPS.

"Lunit has demonstrated through several abstracts the credibility of Lunit SCOPE IO as a companion diagnostic tool to predict immunotherapy treatment outcomes," said Chan-Young Ock, Chief Medical Officer at Lunit. "This study is a proof-of-concept that compiles all of our past research that elucidates Lunit AI's ability to optimize cancer treatment selection."

Last year, Lunit announced a strategic investment of USD 26 million from Guardant Health, Inc., a leading precision oncology company. Following this major collaboration intended to reshape and innovate the precision oncology landscape, Lunit continues to refine its global position by validating the effectiveness of its AI technology through various studies.

SOURCE Lunit

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Breakthrough Study Validates Artificial Intelligence as a Novel Biomarker in Predicting Immunotherapy Response - Published in Journal of Clinical...

The European approach to artificial intelligence (AI) will help build a resilient Europe for the Digital Decade where people and businesses can enjoy the benefits of AI. It focuses on 2 areas: excellence in AI and trustworthy AI. The European approach to AI will ensure that any AI improvements are based on rules that safeguard the functioning of markets and the public sector, and peoples safety and fundamental rights.

To help further define its vision for AI, the European Commission developed an AI strategy to go hand in hand with the European approach to AI. The AI strategy proposed measures to streamline research, as well as policy options for AI regulation, which fed into work on the AI package.

The Commission published its AI package in April 2021, proposing new rules and actions to turn Europe into the global hub for trustworthy AI. This package consisted of:

Fostering excellence in AI will strengthen Europes potential to compete globally.

The EU will achieve this by:

The Commission and Member States agreed boost excellence in AI by joiningforces on AI policy and investment. The revised Coordinated Plan on AI outlines a vision to accelerate, act, and align priorities with the current European and global AI landscape and bring AI strategy into action.

Maximising resources and coordinating investments is a critical component of the Commissions AI strategy. Through the Digital Europe and Horizon Europe programmes, the Commission plans to invest 1 billion per year in AI. It will mobilise additional investments from the private sector and the Member States in order to reach an annual investment volume of 20 billion over the course of the digital decade.

The newly adopted Recovery and Resilience Facility makes 134 billion available for digital. This will be a game-changer, allowing Europe to amplify its ambitions and become a global leader in developing cutting-edge, trustworthy AI.

Access to high quality data is an essential factor in building high performance, robust AI systems. Initiatives such as the EU Cybersecurity Strategy, the Digital Services Act and the Digital Markets Act, and the Data Governance Act provide the right infrastructure for building such systems.

Building trustworthy AI will create a safe and innovation-friendly environment for users, developers and deployers.

The Commission has proposed 3 inter-related legal initiatives that will contribute to building trustworthy AI:

The Commission aims to address the risks generated by specific uses of AI through a set of complementary, proportionate and flexible rules. These rules will also provide Europe with a leading role in setting the global gold standard.

This framework gives AI developers, deployers and users the clarity they need by intervening only in those cases that existing national and EU legislations do not cover. The legal framework for AI proposes a clear, easy to understand approach, based on four different levels of risk: unacceptable risk, high risk, limited risk, and minimal risk.

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A European approach to artificial intelligence | Shaping ...

How are Alexa, Siri and artificial intelligence (AI) impacting and intervening in dangerous situations in daily life? Thats an evolving issue that SUNY Oswego communication studies faculty member Jason Zenor continues to explore, including in an award-winning publication.

In If You See Something, Say Something: Can Artificial Intelligence Have a Duty to Report Dangerous Behavior in the Home, published in the Denver Law Review, Zenor recounted a 2017 incident where police reported a jealous man threatening his girlfriend at gunpoint unknowingly caused their Amazon Echos Alexa to call the police, leading to his arrest.

While the incident made national news - in part because of its relative rarity - Zenor noted it represents the tip of an iceberg for how AI evolves to interact with daily online activity.

You can find a few dozen stories over the last several years where Siri or Alexa save a life, such as with crime, accidents, heart attacks or the like, Zenor explained. In those situations the victim has their phone or in-home device set up to recognize Call 911 or emergency. This is a simple setting and most are now set up for this automatically.

Zenors publication, recognized as a top paper in the 2021 National Communication Association Conferences Freedom of Expression division, explored the trend further, and his research found that smartphones and in-home devices are not capable of enabling anything beyond direct requests to call 911. But artificial intelligence is at work behind the scenes in other situations.

Facebook and other tech companies can monitor things like bullying, hate speech and suicidal tendencies in online spaces through AI, Zenor noted. But it is still looking for certain words and will respond with pre-approved responses like hotline numbers. In-home AI and other devices are set up to listen when we want them to -- but it still needs certain prompts, though the language ability is getting better.

AI is not yet making a big difference in home safety - other than in-home audio and video, as after-the-fact evidence - because of the complicated nature of doing, while in fact, it is more likely right now that perpetrators will use apps to track and surveil their victims than it is that an AI will help a victim, although certainly not proactively, Zenor noted. But the field is making strides elsewhere.

Outside the home, predictive AI is being used in both health care and law enforcement, Zenor This is admirable in health care and similar to screenings that health care facilities now give to patients such as depression, drug abuse or safety in the home. But with both of these spheres, it is only predictive and we also run into issues of implicit bias programmed into AI leading to disparate treatment based on race, sexuality, income and other factors, and this is already happening in the justice system. Any time someone is reported it can lead to unnecessary involvement with law enforcement or mental health systems that changes the trajectory of someone's life. This can have grave consequences.

Related to that, these questions also take into account such legal issues as privacy, criminal procedure, duty to report and liability.

The first question that will need to be answered is what is the 'status' of our AI companions, Zenor explained. The courts are slowly giving more privacy protection to our connected devices. No longer can law enforcement simply just ask the tech companies for the data. But if AI advances to be more anthropomorphic and less of a piece of tech, then the question is what is the legal parallel? Is it law enforcement seizing our possessions -- as it does with phones and records -- or will the in-home AI be more like a neighbor or family member reporting us? The former invokes the Fourth Amendment, the latter does not, as committing a crime or harm is not protected by general privacy laws.

The other side of the coin involves proactive duties to report. Generally, people have no duty to report, Zenor said. The exception is certain relationships - such as teachers, doctors or parents - who would have a duty to report possible harms when it comes to those to whom they have a responsibility such as students, patients or children.

Liability issues could complicate the picture even further, and could lead to unexpected lawsuits for companies using AI.

Once you do act, then you do have a duty of due care, Zenor said. If you do not use due care and it leads to an injury, then there could be liability. So, companies may open themselves up to liability if they program AI to be able to respond and it goes wrong. Conversely, if the companies could program AI to do this and choose not to, then there will certainly be at a minimum PR issues, but I could see it turning into class action negligence cases when deaths do occur.

Like many issues related to evolution of technology, individuals and society have to consider trade-offs.

Ultimately, we have to consider how much more encroachment into our private lives we are willing to accept in exchange for protecting us from harm, Zenor noted. This is not a new question it arises everytime we have an advancement in technology. Ultimately, privacy is a social construction in the law -- what we as a society consider to be the boundaries. We seem to become more comfortable as time passes and technology natives see no issue while older generations think of it as gross violation.

As for the future of how and how often AI will intervene while attempting to provide help?

My best guess is that there will be incidents that make the news where AI saves a life and there will be public pressure to add more safety features to the technology, Zenor said. AI will advance enough that machines become companions like our pets so we will have a relationship with them that includes divulging private information that it could keep permanently. As it is today, we would expect that if our companion could save us, then they will try to many people own pets as a form of protection or as service pet. The big issue from this will be liability. I assume companies will seek out liability protections either through waivers in terms of agreement or through special legislation similar to 'good samaritan' laws.

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Digital assistants, artificial intelligence and the blurred lines of intervention - SUNY Oswego

AI is set to continue disrupting business operations, and increasingly drive value.

This article will explore how artificial intelligence is set to impact organisations in the future, gauging the insights of experts in the space.

Artificial intelligence (AI) is changing how businesses work and interact with their processes, products and people on both the employee and client side of operations. Gartner predicts the worldwide AI software market to reach $62 billion in 2022, an increase of over 20%. This digitisation is game-changing for companies in all sectors, as it underpins smarter, more streamlined and more cost-effective running of businesses, as well as driving more agile operations in todays disruptive climate.

With this in mind, we take a look at the possible future impact of artificial intelligence, as the technology continues to develop and infiltrate more business use cases.

Five AI predictions for 2022: from enterprise to everyday AI

Florian Douetteau, CEO of Dataiku, provides his five enterprise and everyday AI predictions for 2022. Read here

Organisations of all sizes, across multiple sectors, look set to continue deploying AI as part of their business strategy. By taking a step back and applying a joined-up, strategic approach to implementing AI-enhanced technologies such as intelligent automation, leaders can derive clear business benefits, including but not limited to improved customer service, increased competitiveness, greater productivity, and a more satisfied workforce.

Eric Tyree, head of research and AI at Blue Prism, explained: Whether its cutting customer wait times in financial services, enabling a more resilient and agile supply chain, or improving patient care by minimising manual admin work, intelligent automation can be the key driver in achieving strategic corporate initiatives.

Being ahead of the curve in this respect will have a significant impact on an organisations competitiveness within the market.

With AI-powered technologies in place to improve processes and transformation, organisations can then re-imagine how they operate, using a digital-first mindset. This, in turn, will allow staff to focus on more purposeful duties, including those focusing on customer service, and less on administrative functions.

Tyree continued: Relatively speaking, intelligent automation technology is the easy part of process improvement and transformation. Intelligent automation makes the implementation of operational reimagination much simpler and is having a huge impact on the way businesses are looking at their workforce, ways of working and capability to enact change that has strategic value to the business.

The capabilities digital robots possess lets the technology do the heavy lifting, allowing employees the capacity to take on more meaningful and complex work. The emphasis becomes a shift of human capital towards revenue generating or customer-centric activity, which gives way to enhanced capacity, more fulfilling work for staff, and more agility and scalability of resource across the entire organisation.

As more businesses make the commitment to AI and other transformative technologies, we will see this impact more and more organisations for the better across the globe.

In todays fast-paced digital and commercial world, organisations rely on networks to operate day-to-day. However, to deploy the networking services needed to meet the demands of this new hybrid working world will now need a network that utilises artificial intelligence and other autonomous capabilities.

Automation itself, and the idea that technologies can be self-provisioning, self-diagnosing, and self-healing, has existed for some time, explained John Morrison, senior vice-president of international markets at Extreme Networks.

But, thanks to advances in Artificial Intelligence (AI), autonomous networks are now becoming a reality. An autonomous network runs with minimal to no human intervention by configuring, monitoring, and maintaining itself independently. AI is now having a significant impact on businesses by replacing restrictive, error-prone networks and relieving overburdened IT teams tasked with finding and fixing problems instead of empowering and enabling people and connections.

Everyone can benefit from autonomous networks, driven by AI. For healthcare institutions, such networks have the ability to, for example, connect a medivac chopper to the doctors on the helipad or monitor the IV pumps that keep a patient alive. And for schools, a connected classroom can be created to help children overcome learning challenges through supportive software or monitor attendance to proactively keep at-risk students engaged in education.

Understanding your automation journey

Ravi Dirckze, technical product manager at HelpSystems, explains the need for businesses to understand their automation journey. Read here

On the customer side of things, users of digital services have been benefitting from AI deployments, which are proving to make engagement more efficient. While still relatively early in its development, artificial intelligence looks set to support bolstered personalisation and customisation over time.

As we enter the new metaverse era, we will only have access to increasing data points, meaning well be able to use AI more efficiently to create tailored experiences for customers, said Maja Schaefer, CEO and founder of Zowie.

In the future, our experiences with a brand will always be customised. For instance, when you enter a supermarket in the metaverse, the shelves will be stacked differently for others.

AI is already driving better online recommendations and targeted ads. In the future, it will go beyond and impact interactions as well. Over the last few years we have seen more and more practical applications of AI technology, and in the coming years, it will become widespread. As AI becomes part of our everyday life, its important to remember and care for privacy. Data being processed should always be anonymised and used only for specific purposes.

As AI continues to develop over the coming years, it will disrupt more operations across more sectors, leading to increased efficiency and decreased strain on workers going forward. The biggest impact from AI is set to come from those companies that can move their models into production most efficiently, and find ways to integrate those models best with their existing business processes.

Alex Housley, CEO and founder of Seldon, commented: The highest transformational potential for AI likely lies in healthcare; despite currently sitting at a 36% adoption rate, healthcare applications such as improved diagnostics or protein folding can deliver exceptional social and economic returns.

Were also beginning to see other industries like construction and logistics leverage ML models to optimise their services. For example, the construction industry is using ML to increase the accuracy of estimates when planning projects and improve safety by detecting potential risks on-site to prevent accidents.

Were also seeing AI perform better, owing to improvements in how developers create models, alongside the ability for us to compress models and run them on edge hardware allowing a greater variety of applications. AI is also becoming more accessible due to technologies like AI marketplaces, AI maker/teacher kits, and low-code/no-code AI platforms.

All in all, these improvements dramatically improved AIs use in industry, with nearly one in three enterprises having a model production by the end of 2021.

Manufacturing will see great potential for innovation, through an emerging framework called machine health. This capability uses the Internet of Things (IoT) and AI to predict and prevent industrial machine failures, and improve machine performance, via analytics.

Saar Yoskovitz, CEO and co-founder of Augury, expanded on how this is set to impact manufacturing operations in the future: AI is spearheading the fourth industrial revolution alongside technologies like automation and IoT capabilities.

Manufacturing is one of the industries already seeing a great benefit as AI is used to provide greater visibility into the processes, efficiency and capacity of these businesses. A key example is machine health a solution driven by AI to provide predictive analytics on critical and supporting equipment within manufacturing plants.

Sensors capture vibration, temperature and magnetic data from industrial machines, AI diagnoses machine issues based on that data and input from human reliability experts, explains what caused them and prescribes courses of action.

The impact of this AI use case is huge. When a critical machine fails, an entire production line grinds to a halt and that has serious upstream and downstream effects on entire supply chains. Machine health, therefore, allows manufacturers to strengthen their resilience against supply chain issues, or global events that impact production.

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The future impact of artificial intelligence - Information Age