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Machine Learning in Finance Market 2023 Objectives of the Study, Research Methodology and Assumptions, Value Chain Analysis and Forecast by 2032

The industry's behavior is discussed in detail. It also outlines the future direction that will ensure strong profits over the coming years. This report will provide a practical overview of the global market and its changing environment to help readers make informed decisions about market projects. This report will focus on that will allow the market to expand its operations in existing markets.

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This report helps both to analyze the market in-depth. This will help the leading players decide on their business strategy and set goals. This report provides critical market information, including Machine Learning in Finance market size, growth rates and forecasts in key regions and countries, as well as growth opportunities in niche markets.

The Machine Learning in Finance report contains data based on - using proven research methods. This report provides all-around information that aids in the estimation of every part of the Machine Learning in Finance market. This report was created by considering several aspects of market research and analysis. These include market size estimates, market dynamics, company and market best practices. Entry-level marketing strategies, positioning, segmentation, competitive landscaping and economic forecasting. Industry-specific technology solutions, roadmap analysis, targeting key buying criteria and in-depth benchmarking of vendor offerings.

Ignite Ltd Yodlee Trill A.I. MindTitan Accenture (NYS:ACN) ZestFinance

Machine Learning in Finance Based on Type:

Supervised Learning Unsupervised Learning Semi Supervised Learning Reinforced Leaning

Machine Learning in Finance By Application

Banks Securities Company Others

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- North America (the U.S and Canada and the rest of North America)

- Europe (Germany, France, Italy and Rest of Europe)

- Asia-Pacific (China, Japan, India, South Korea and Rest of Asia-Pacific)

- LAMEA (Machine Learning in Financezil, Turkey, Saudi Arabia, South Africa and Rest of LAMEA)

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:

1. Industry trends (2015-2020 historic and future 2022-2031)

2. Key regulations

3. Technology roadmap

4. Intellectual property analysis

5. Value chain analysis

6. Porters Five Forces Model, PESTLE and SWOT Analysis

:

How is the Machine Learning in Finance market along with regions like , , -, are growing?

What - are responsible for driving market growth?

What are the of Cognitive Mediamarket? What growth prospects are there for the market applications?

What stage are the key products on the Machine Learning in Finance market?

What are the challenges that the Global (North America and Europe and Asia-Pacific and South America) must overcome to be commercially viable? Are their growth and commercialization dependent on cost declines or technological/application breakthroughs?

What are the prospects for the Machine Learning in Finance Market?

What is the difference between performance characteristics of Machine Learning in Finance and established entities?

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3. - for Machine Learning in Finance Market.

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6. It provides a regional analysis of the Machine Learning in Finance Market as well as business profiles for several stakeholders.

7. It provides massive data on trending factors that can influence the development of the Machine Learning in Finance Market.

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Machine Learning in Finance Market 2023 Analytical Assessment, Key Drivers, Growth and Opportunities to 2032 - EIN News

Philogen

Philogen Announces Publication of a New Study in Collaboration with Google focused on Machine Learning models applied to DNA-Encoded Chemical Library Technology

The collaboration has focused on the use of Googles Machine Learning models combined with Philochems DNA-Encoded Chemical Libraries

The research activity promises to have a direct impact on the discovery of novel tumor-targeting small organic ligands with broad applicability in a number of different indications

Siena, Italy, March 28, 2023 - Philogen S.p.A., a clinical-stage biotechnology company focused on the development of innovative antibody and small molecule ligands, announces the publication of a new study conducted in collaboration with Google focused on the use of Machine Learning models applied to the screening of DNA-Encoded Chemical Libraries (DELs). Philogen practices DEL technology within its fully-owned Swiss-based Philochem AG subsidiary.

DELs emerged as an efficient and cost-effective ligand discovery tool. The technology allows the rapid selection of specific binders (Phenotype), physically connected to unique DNA tags (Genotype) that work as amplifiable identification barcodes. Philochem has synthesized several DNA-Encoded Chemical Libraries, featuring different designs, that have yielded high affinity and selective binders to a variety of target proteins of pharmaceutical interest.

Results of DEL selection are extremely data-rich, as they may contain enrichment information for billions of compounds on a variety of different targets. In principle, this information can be exploited using computational methods both for the affinity maturation of DEL-derived HIT compounds and for the characterization of binding specificities.

In this collaborative project, Google and Philochem, a fully-owned subsidiary of Philogen, have applied DEL Technology and Instance-Level Deep Learning Modelling to identify tumor-targeting ligands against Carbonic Anhydrase IX (CAIX), a clinically validated marker of hypoxia and of clear cell Renal Cell Carcinoma. The approach yielded binders that showed accumulation on the surface of CAIX-expressing tumor cells in cellular binding assays. The best compound displayed a binding affinity of 5.7 nM and showed preferential tumor accumulation in in vivo pre-clinical models of Renal Cell Carcinoma.

Story continues

The successful translation of LEAD candidates for in vivo tumor-targeting applications demonstrates the potential of using machine learning with DEL Technology to advance real world drug discovery.

The results of the study are available as preprint on the BioRXiv website at http://www.biorxiv.org/content/10.1101/2023.01.25.525453v1.

Dario Neri, Chief Executive Officer of Philogen commented: We are excited by the potential of the synergy between DNA-Encoded Chemical Libraries and Artificial Intelligence. The powerful discovery approach that we have developed together with Google should be broadly applicable to additional targets of pharmaceutical interest for the discovery of novel drug prototypes.

Philogen Group Description

Philogen is an Italian-Swiss company active in the biotechnology sector, specialized in the research and development of pharmaceutical products for the treatment of highly lethal diseases. The Group focuses on the discovery and development of targeted anticancer drugs, exploiting high-affinity ligands for tumor markers (also called tumor antigens). These ligands - human monoclonal antibodies or small organic molecules - are identified using Antibody Phage Display Libraries and DNA-Encoded Chemical Library technologies.

The Group's main therapeutic strategy for the treatment of these diseases is represented by the concept of tumor targeting. This approach is based on the use of ligands capable of selectively delivering very potent therapeutic active ingredients (such as pro-inflammatory cytokines) to the tumor mass, sparing healthy tissues. Over the years, Philogen has mainly developed monoclonal antibody-based ligands that are specific for antigens expressed in tumor-associated blood vessels, but not expressed in blood vessels associated with healthy tissues. These antigens are usually more abundant, more stable and better accessible than those expressed directly on the surface of tumor cells. The elaborate expertise in the field of vascular targeting enabled the generation of a strong portfolio with many ongoing projects which are currently pursued by the Group.

The Group's objective is to generate, develop and market innovative products for the treatment of diseases for which medical science has not yet identified satisfactory therapies. This is achieved by exploiting (i) proprietary technologies for the isolation of ligands that react with antigens present in certain diseases, (ii) experience in the development of products which selectively accumulate at the disease sites, (iii) experience in drug manufacturing and development, and (iv) an extensive portfolio of patents and intellectual property rights.

Although the Group's drugs are primarily oncology applications, the targeting approach is also potentially applicable to other diseases, such as certain chronic inflammatory diseases.

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Philogen - Investor Relations

IR@philogen.com - Emanuele Puca | Investor Relations

Consilium Strategic Communications contacts

Mary-Jane Elliott, Davide Salvi

Philogen@consilium-comms.com

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Philogen Announces Publication of a New Study in Collaboration with Google focused on Machine Learning models applied to DNA-Encoded Chemical Library...

Mind reading is common among us humans. Not in the ways that psychics claim to do it, by gaining access to the warm streams of consciousness that fill every individuals experience, or in the ways that mentalists claim to do it, by pulling a thought out of your head at will. Everyday mind reading is more subtle: We take in peoples faces and movements, listen to their words and then decide or intuit what might be going on in their heads.

Among psychologists, such intuitive psychology the ability to attribute to other people mental states different from our own is called theory of mind, and its absence or impairment has been linked to autism, schizophrenia and other developmental disorders. Theory of mind helps us communicate with and understand one another; it allows us to enjoy literature and movies, play games and make sense of our social surroundings. In many ways, the capacity is an essential part of being human.

What if a machine could read minds, too?

Recently, Michal Kosinski, a psychologist at the Stanford Graduate School of Business, made just that argument: that large language models like OpenAIs ChatGPT and GPT-4 next-word prediction machines trained on vast amounts of text from the internet have developed theory of mind. His studies have not been peer reviewed, but they prompted scrutiny and conversation among cognitive scientists, who have been trying to take the often asked question these days Can ChatGPT do this? and move it into the realm of more robust scientific inquiry. What capacities do these models have, and how might they change our understanding of our own minds?

Psychologists wouldnt accept any claim about the capacities of young children just based on anecdotes about your interactions with them, which is what seems to be happening with ChatGPT, said Alison Gopnik, a psychologist at the University of California, Berkeley and one of the first researchers to look into theory of mind in the 1980s. You have to do quite careful and rigorous tests.

Dr. Kosinskis previous research showed that neural networks trained to analyze facial features like nose shape, head angle and emotional expression could predict peoples political views and sexual orientation with a startling degree of accuracy (about 72 percent in the first case and about 80 percent in the second case). His recent work on large language models uses classic theory of mind tests that measure the ability of children to attribute false beliefs to other people.

A brave new world. A new crop of chatbotspowered by artificial intelligence has ignited a scramble to determine whether the technology could upend the economics of the internet, turning todays powerhouses into has-beens and creating the industrys next giants. Here are the bots to know:

ChatGPT. ChatGPT, the artificial intelligence language model from a research lab, OpenAI, has been making headlines since November for its ability to respond to complex questions, write poetry, generate code, plan vacationsand translate languages. GPT-4, the latest version introduced in mid-March, can even respond to images(and ace the Uniform Bar Exam).

Bing. Two months after ChatGPTs debut, Microsoft, OpenAIs primary investor and partner, added a similar chatbot, capable of having open-ended text conversations on virtually any topic, to its Bing internet search engine. But it was the bots occasionally inaccurate, misleading and weird responsesthat drew much of the attention after its release.

Ernie. The search giant Baidu unveiled Chinas first major rival to ChatGPT in March. The debut of Ernie, short for Enhanced Representation through Knowledge Integration, turned out to be a flopafter a promised live demonstration of the bot was revealed to have been recorded.

A famous example is the Sally-Anne test, in which a girl, Anne, moves a marble from a basket to a box when another girl, Sally, isnt looking. To know where Sally will look for the marble, researchers claimed, a viewer would have to exercise theory of mind, reasoning about Sallys perceptual evidence and belief formation: Sally didnt see Anne move the marble to the box, so she still believes it is where she last left it, in the basket.

Dr. Kosinski presented 10 large language models with 40 unique variations of these theory of mind tests descriptions of situations like the Sally-Anne test, in which a person (Sally) forms a false belief. Then he asked the models questions about those situations, prodding them to see whether they would attribute false beliefs to the characters involved and accurately predict their behavior. He found that GPT-3.5, released in November 2022, did so 90 percent of the time, and GPT-4, released in March 2023, did so 95 percent of the time.

The conclusion? Machines have theory of mind.

But soon after these results were released, Tomer Ullman, a psychologist at Harvard University, responded with a set of his own experiments, showing that small adjustments in the prompts could completely change the answers generated by even the most sophisticated large language models. If a container was described as transparent, the machines would fail to infer that someone could see into it. The machines had difficulty taking into account the testimony of people in these situations, and sometimes couldnt distinguish between an object being inside a container and being on top of it.

Maarten Sap, a computer scientist at Carnegie Mellon University, fed more than 1,000 theory of mind tests into large language models and found that the most advanced transformers, like ChatGPT and GPT-4, passed only about 70 percent of the time. (In other words, they were 70 percent successful at attributing false beliefs to the people described in the test situations.) The discrepancy between his data and Dr. Kosinskis could come down to differences in the testing, but Dr. Sap said that even passing 95 percent of the time would not be evidence of real theory of mind. Machines usually fail in a patterned way, unable to engage in abstract reasoning and often making spurious correlations, he said.

Dr. Ullman noted that machine learning researchers have struggled over the past couple of decades to capture the flexibility of human knowledge in computer models. This difficulty has been a shadow finding, he said, hanging behind every exciting innovation. Researchers have shown that language models will often give wrong or irrelevant answers when primed with unnecessary information before a question is posed; some chatbots were so thrown off by hypothetical discussions about talking birds that they eventually claimed that birds could speak. Because their reasoning is sensitive to small changes in their inputs, scientists have called the knowledge of these machines brittle.

Dr. Gopnik compared the theory of mind of large language models to her own understanding of general relativity. I have read enough to know what the words are, she said. But if you asked me to make a new prediction or to say what Einsteins theory tells us about a new phenomenon, Id be stumped because I dont really have the theory in my head. By contrast, she said, human theory of mind is linked with other common-sense reasoning mechanisms; it stands strong in the face of scrutiny.

In general, Dr. Kosinskis work and the responses to it fit into the debate about whether the capacities of these machines can be compared to the capacities of humans a debate that divides researchers who work on natural language processing. Are these machines stochastic parrots, or alien intelligences, or fraudulent tricksters? A 2022 survey of the field found that, of the 480 researchers who responded, 51 percent believed that large language models could eventually understand natural language in some nontrivial sense, and 49 percent believed that they could not.

Dr. Ullman doesnt discount the possibility of machine understanding or machine theory of mind, but he is wary of attributing human capacities to nonhuman things. He noted a famous 1944 study by Fritz Heider and Marianne Simmel, in which participants were shown an animated movie of two triangles and a circle interacting. When the subjects were asked to write down what transpired in the movie, nearly all described the shapes as people.

Lovers in the two-dimensional world, no doubt; little triangle number-two and sweet circle, one participant wrote. Triangle-one (hereafter known as the villain) spies the young love. Ah!

Its natural and often socially required to explain human behavior by talking about beliefs, desires, intentions and thoughts. This tendency is central to who we are so central that we sometimes try to read the minds of things that dont have minds, at least not minds like our own.

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Have AI Chatbots Developed Theory of Mind? What We Do and Do Not Know. - The New York Times

This article has been reviewed according to ScienceX's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility:

Illustration of the Support Vector Machine (SVM) Algorithm. The Black Circles and Triangles Indicate Unaffected Individuals and Patients with CVD, Respectively. A: Normal people and CVD patients are linearly separable. B: Normal people and CVD patients are nonlinearly separable. C: Normal people and CVD patients are mapped into high-dimensional space and separated by a decision surface. Credit: Cardiovascular Innovations and Applications (2023). DOI: 10.15212/CVIA.2023.0011

Cardiovascular disease (CVD) is one of the leading causes of death worldwide, and answers are urgently needed regarding many aspects, particularly risk identification and prognosis prediction. Real-world studies with large numbers of observations provide an important basis for CVD research but are constrained by high dimensionality, and missing or unstructured data.

Machine learning (ML) methods, including a variety of supervised and unsupervised algorithms, are useful for data governance, and are effective for high dimensional data analysis and imputation in real-world studies. This article reviews the theory, strengths and limitations, and applications of several commonly used ML methods in the CVD field, to provide a reference for further application.

This article introduces the origin, purpose, theory, advantages and limitations, and applications of multiple commonly used ML algorithms, including hierarchical and k-means clustering, principal component analysis, random forest, support vector machine, and neural networks. An example uses a random forest on the Systolic Blood Pressure Intervention Trial (SPRINT) data to demonstrate the process and main results of ML application in CVD.

ML methods are effective tools for producing real-world evidence to support clinical decisions and meet clinical needs. This review explains the principles of multiple ML methods in plain language, to provide a reference for further application. Future research is warranted to develop accurate ensemble learning methods for wide application in the medical field.

The study is published in the journal Cardiovascular Innovations and Applications.

More information: Jiawei Zhou et al, Machine Learning Methods in Real-World Studies of Cardiovascular Disease, Cardiovascular Innovations and Applications (2023). DOI: 10.15212/CVIA.2023.0011

Provided by Compuscript Ltd

Originally posted here:
Machine learning methods in real-world studies of cardiovascular disease - Medical Xpress

In aglobal reportissued by S&P, 95% of enterprises across various industries said that Artificial Intelligence (AI) adoption is an important part of their digital transformation journey. Were seeing expanded interest in the adoption of AI for many reasons, including lowering costs, increasing sales, and improving worker productivity. At the same time, if youre keeping up with the news on AI these days, you know were also seeing considerable focus placed on explaining how AI models work and why explainability is important. But our question as two AI practitioners Is explainability that important? Or does it lead to a false sense of security?

Explainable Artificial Intelligence (XAI), as summed up by IBM Watson, is a set of processes and methods that allows human users to comprehend and trust the results and outputcreated by machine learning algorithms.Many believe that XAI promotes model transparency and trust, making people more comfortable with the risk of improper learning and incorrect predictions that can occur with machine learning models.

Its human nature to seek explanations as a means of better understanding unknown subjects. We lean on explainability even more when the stakes are high. Asrecently concluded by two Dartmouth researchers, if the explanation is visually supported by pretty charts, we are partial to it. Explanations can give us a feeling of security when it comes to making informed decisions. Take, for example, a patient who asks a doctor for an explanation of a diagnosis. Even when the explanation is hard to grasp, the more scientific the doctor sounds, the better the patient may feel. It can be the same with AI. The more detail end users are given about how it works, the more likely they are to accept the outcome as valid and feel confident about doing so.

Are explanations sufficient? Some things are complex, and merely having an explanation is not a sufficient and necessary condition to derive utility.

And with many businesses considering avenues for AI adoption, we have to ask about the risks associated with relying so heavily on explainability. What if the explainer is not sufficiently knowledgeable? Users could be fed incorrect information without realizing it. What if theres not familiarity with the topic to fully grasp the explanation? It is quite possible that when it comes to new topics like AI models, users such as business stakeholders, regulators, and even domain experts may end up with only a superficial understanding of the explanation provided. They may not be able to discern if and how the model was incorrect in the first place, which means even with explanations, users can still end up with disastrous decision making.

In many use cases, a more accurate model is better than having an explanation. After all, what better evidence of utility than a model that gives the right outcome? Hence, we must question if we should be going after explainability, as is the rage right now in XAI, or after truthfulness?

Truthfulness comes from accuracy measures, which give us an indication of how much reliance we can place on the system. Accuracy is directly linked to the quality of the underlying data. The progression of data quality and accuracy over time goes hand and hand. Many AI models are used in dynamic settings where data drift is the norm. Asking crucial questions about the distribution of training data and out of sample data is elemental to having accurate models that can be relied on.

Forget explanations and reasoning for a moment and picture a system that can establish a high degree of truthfulness by means of doing well on a large test dataset across different real-world distributions. Seems too good to be true, right?

Let us examine this concept using a real-life scenario. Have you ever had to ask your colleague or friend for an explanation of how they recognized you in just a nanosecond of time? No, because of the truthfulness of the outcome. It never crosses your mind to understand the how, because the end result is correct with a high degree of accuracy. Similarly in AI, when we transition to a phase where the models accuracy beats the human baseline, and we reach that high degree of accuracy, explainability will become less relevant. So, what is the alternative to explainability? Simplified, business-friendly metrics. As AI practitioners, we need to recognize that it is difficult for non-practitioners to make sense of our different analytical metrics, such as: F1 Score, Rouge Score, Perplexity, Bleu Score, WER, Confusion Matrix, etc. We need a simplified, business-friendly metric that can be readily understood, like Googles use of Sensibleness and Specificity Average (SSA) Score in their evaluation score for Meena.[1]While it may not be easy to develop simplified metrics in all instances, its imperative we do so whenever possible to limit the need for model explanations and ultimately lead to better decision-making for AI end users.

Link:
The illusion of explainability in machine learning models - Finextra