The Generative AI News (GAIN) rundown for July 20, 2023, delves into the recent advancements in the field of generative artificial intelligence. The article sheds light on Metas Llama 2, an open source and free tool in the LLM market, and discusses its potential impact. Additionally, it examines the alleged performance degradation of GPT-4, scrutinizing the research paper results and contemplating a potential rebuttal. The article also features insights into the winners and losers of the week in the generative AI sector.

Metas decision to make Llama 2, a tool in the LLM market, open source and free for commercial use, is anticipated to have profound implications for the industry. This move may democratize access to advanced language models and foster innovation.

Claims surrounding GPT-4s performance degradation have been circulating, prompting the article to analyze the research paper results and question the methodology employed. The possibility of a rebuttal is also explored, raising doubts about the alleged decline in performance.

Furthermore, the article highlights the winners and losers of the week in the generative AI space, offering valuable insights into the latest developments and industry trends. It provides readers with a comprehensive overview of the progress made in the field.

In addition to the aforementioned topics, the article also covers funding news in the industry. Cognaize secured $18 million to develop an enhanced LLM for the finance sector, while Preply closed a Series C funding round, raising $120 million and focusing on strengthening its AI capabilities. Moreover, SAPs investments in Aleph Alpha, Anthropic, and Cohere are mentioned.

To conclude, this article offers a comprehensive overview of the latest advancements in generative artificial intelligence. It examines the potential ramifications of Metas decision to open source Llama 2, scrutinizes the claims concerning GPT-4s performance degradation, and provides insights into the winners and losers of the week in the generative AI sector. The funding news and investments in the industry are also covered. For readers intrigued by these topics, the provided links offer avenues for further exploration.

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Latest Developments in Generative Artificial Intelligence - Fagen wasanni

We proposed the PCAIDS, an AutoML-based model, for the prediction of csPCa based on quick and economic routinely performed clinical examinations. The PCAIDS incorporated multimodal and multidimensional data, including laboratory tests, imaging tests, and demographic data, revealing encouraging discriminative power with AUCs of 0.820 in the validation cohort and 0.807 and 0.850 in the two prospective test cohorts.

Compared with previous prediction models, such as the ERSPC-RC [14], PCPT-RC [15] and CPCC-RC [16], the PCAIDS, for the first time, evaluated over 100 multimodal features with AI-based algorithms. These features, including demographics, laboratory tests, and imaging examinations, were assessed by a series of AI algorithms. Among these AI algorithms, AutoML outperformed logistic regression, random forest, and XGBoost. AutoML has become a popular and efficient modeling tool for data science that uses k-fold cross-validation through varying optimization algorithms, such as grid search, random search, and genetic algorithm (GA), to scan different feature combinations, feature transformations, supervised algorithms, and their corresponding hyperparameter combinations implemented in AutoWEKA [17], Autogluon [18], AutoSklearn 2.0 [19], and TPOT, [20] thereby identifying the optimal machine learning pipeline.

Additionally, AI-based methods have the potential to analyze high-volume data and to discover nonlinear and interactive prediction information. For cancer diagnosis, there were huge possibilities that currently applied predictive models only included a proportion of effective predictors. Although the application of AI-based methods may not always outperform linear models, the advantage of involving more features could help the models to be more stable and more applicable for different populations.

In this aspect, Jungyo Suh et al. proposed the possibility of applying AI-based algorithms in the prediction of prostate biopsy. They developed an AI-based prediction tool with PSA, total prostate volume, age, hypoechoic lesion on ultrasonography, transitional zone volume, testosterone, and fPSA [21]. This study showed the promising future of using AI-based algorithms in predicting PCa; however, the investigated features were of limited number. To some extent, AI-based algorithms were not ideal for the analysis of limited features, which could have been done by traditional methods. In predicting colon cancer, researchers applied AI-based methods to data from health maintenance organizations by evaluating analytes from standard laboratory records, including hematology, liver function, and metabolism [22]. In breast cancer, the notion of applying AI-based methods to diagnose breast cancer was validated, and age, body mass index (BMI), glucose, insulin, homeostasis model assessment (HOMA), leptin, adiponectin, resistin and chemokine monocyte chemoattractant protein 1 (MCP1) attributes were used in the prediction model [23]. Further studies validated that routine blood analysis features had a boosted performance for breast cancer diagnosis and supported the notion that this approach is of great potential to be used in a widespread manner to detect cancers [24]. These studies suggested the possibility of using routine health examinations to predict cancer based on AI algorithms.

The clinical scenario for the application of PCAIDS is between PSA-based screening and novel tests predicting PCa, including mpMRI, urinary PCA3 test, 4kScore, and Prostate Health Index. MpMRI, a potent modality in predicting biopsy results, is of great importance in patients who are at high risk of PCa. However, the application of mpMRI is limited by the accessibility of MRI machines and the professionalism of the radiologists who interpret the images. Meanwhile, these biomarkers were only available for patients in some countries and regions. In addition, mpMRI and these novel biomarkers are associated with high costs in most countries. The application of PCAIDS, on the other hand, does not require special examination equipment. The features included in the model were common, routinely performed, quick, and economic tests, which were also needed for a general health check-up. The application of B-ultrasound in evaluating the size of the prostate is also accessible for almost every hospital. In general, this AI-based modality is not here to perfect the diagnostic modality with mpMRI and novel biomarkers, rather than replacing them.

AutoML has the flaw of interpretability, which is consistently met with skepticism, similar to other complex models, especially in the medical field. To this end, we applied the SHAP [13] tool to explore the contribution of individual features to the model. To explore the rationality of this contribution, we also examined the interpretability of the LR compared to SHAP (Additional file4: Figure S1). First, the contribution of the key variables (the cross-sectional area of the prostate (B_AREA), AGE, and fPSA) is basically the same in the two prediction modalities. This is similar to the previous conclusions obtained by the RF model (Additional file1: Table S1). Second, the SHAP value from AutoML is roughly the same as the importance of LR calculated by model coefficients. Third, B_AREA is the most important variable. Significantly, the risk of PCa did not increase with B_AREA, which may be due to the increased concertation of PSA produced by a larger prostate, misstating that the risk of PCa and fPSA/tPSA are similar. In addition, age played the second most important role in the prediction model. Thereafter, the risk of PCa increases with age, which is intuitive, and the same holds true for other clinical indicators, although no direct cause can be inferred.

One of the limitations of this study is the lack of a head-to-head comparison with mpMRI or other novel biomarkers. However, the clinical scenario of this prediction mode is not to replace novel diagnostic methods but to assist in decision-making for novel diagnostic methods. In addition, we introduced the dimensions of the prostate from the B ultrasound in the model, and there might be inter- and intrarater differences among different centers in terms of ultrasound results. Furthermore, ultrasound images were not included in this study due to the lack of image storage in all centers. We believe that future studies may incorporate the images captured during ultrasound examinations. The findings of this study are applicable primarily to Asian populations due to the vast discrepancy between Asian and Caucasian patients. In the future, we intend to collect data from various populations to adapt our model to different ethnic groups. Finally, the performance of the PCAIDS is not better than that of the other algorithms, including LR. However, it is important to note that in the study, given the serious implications of missing a prostate cancer diagnosis, prioritizing sensitivity rather than specificity was chosen. This decision was made understanding that it might increase the false positive rate, but it's a reasonable trade-off given the potential severity of a missed diagnosis, where high sensitivity can often lead to lower specificity. We consider that further validation studies may help us to show its wide applicability.

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Artificial intelligence for the diagnosis of clinically significant prostate ... - BMC Medicine

'Knowing ahead of time that a patient may be at risk of harm can help us develop intervention strategies ... and adjustments to their care plan,' says Waypoint official

NEWS RELEASEWAYPOINT CENTRE*************************It's crucial to keep patients safe when they receive care. This is especially important for mental health conditions, where early intervention can make a big difference. In recent years, the application of artificial intelligence (AI) in healthcare has shown great promise, and one area where it holds significant potential is the development of an early warning score (EWS) system for mental health patients.

Early warning scores help care teams identify early signs of a patients health getting worse so they can take action early, said Dr. Andrea Waddell, Medical Director Quality Standards and Clinical Informatics.

Knowing ahead of time that a patient may be at risk of harm can help us develop intervention strategies such as increased nursing attention and adjustments to their care plan.

Data from the Canadian Institute for Health Information in 2021-22 shows that 1 in 17 hospital stays had unintended harm, and almost half of them could have been avoided.

Waypoints Dr. Waddell is also the regional clinical co-lead for mental health and addictions at Ontario Healths Mental Health and Addictions Centre of Excellence. She and her team of researchers are seeking to change this statistic creating an EWS to prevent harm before it happens.

Artificial intelligence has revolutionized various sectors and mental health care is no exception. It can look at a lot of data, find patterns and give helpful information. When used in mental health care, AI can help detect problems early, make personalized treatment plans, and reduce the burden on healthcare providers.

While early warning scores are commonly used in acute medical settings, they havent been used as much in mental health. The EWS system involves always monitoring and analyzing each patient's specific information including historical data and AI algorithms, to understand if they might get worse. Ideally alerting care providers up to 72 hours in advance so they can help the patient sooner.

Waypoint and its expert staff care for some of the provinces most severely ill patients. The hospital has a 20-bed acute mental health program, has submitted a proposal to the Ministry of Health to add an additional 20-bed unit, and is shifting the culture intentionally to become a learning health system; making the hospital uniquely positioned to build this early warning model.

Leveraging existing frameworks, expert opinion, and literature, the hospital is proposing variables for an EWS and testing a machine-learning model on 2022 patient data. Frontline clinicians, patients, and families will provide input at every step to guide the selection of the final algorithm. Once finalized, the EWS will be piloted in some Waypoint units using a rapid-cycle quality improvement model.

Early Intervention and timely detection of deteriorating mental health conditions is really about advancing person-centred care, said Dr. Nadiya Sunderji, President and CEO. Artificial intelligence enables personalized care plans tailored to individual patients' needs, taking into account their specific risk factors, treatment history, and response patterns.

Artificial intelligence unlocks tremendous potential in developing Early Warning Score systems for mental health patients, helping healthcare professionals detect problems early. Leveraging AI's capabilities can enhance patient care, improve outcomes, and reduce the burden on mental health services. AI-driven solutions hold the key to revolutionizing mental health care for a brighter and healthier future.

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Artificial intelligence could aid treatment of mental health issues - MidlandToday

Adam Smith warned of firms within an industry colluding to charge higher prices. Such concerns are magnified in a time of online algorithms and instantaneous price adjustments.

People of the same trade seldom meet together, even for merriment and diversion, but the conversation ends in a conspiracy against the public, or in some contrivance to raise prices.

This famous quotation from The Wealth of Nations was written at a time when cartels were organised in physical, usually smoke-filled, rooms between real people. There are good reasons to think that this is no longer the case.

Economists have long worried about the possibility of tacit collusion, whereby firms adjust their pricing without formally agreeing to do so. This was demonstrated by General Electric (GE) six decades ago. Having been convicted in 1960, along with Westinghouse, of explicit collusion in the market for turbine generators and then having seen prices fall by a half in the following three years GE began to post prices publicly. It released its pricing book, announced its pricing policy, sat back and watched Westinghouse follow suit. The result: the prices and profits of both firms rose.

This example of tacit collusion involved people making decisions. What would Smith make of the possibility of algorithmic collusion that involves no human beings?

This is the idea that firms can set prices online using algorithms that are able to respond to changes in the prices of competing products in real time. It is not difficult to understand why this might allow tacit collusion. Any firm can immediately respond to a competitors price cut, thus removing most of the incentive to lower prices in the first place.

Equally, the algorithm could experiment by hiking prices and seeing if competitors respond. If they do, great. If not, then the algorithm can bring the price straight back down, all within the blink of an eye.

This possibility is a nightmare for competition authorities. Firms reacting to the decisions of their rivals is the essence of price competition. But how do we know when we have too much of it? And what can we do about it? How might authorities stop firms reacting to their rivals price changes?

This is not a far-fetched scenario. The Trod/GB eye cartel case in 2016 in which two online sellers were colluding around the sale of posters and frames on Amazon was all about using online repricing software to monitor the prices of rivals and implement an agreed cartel.

In that case, there was an agreement between humans to create the cartel. But now even that may not be necessary. Recent experimental research shows that relatively simple algorithms based on artificial intelligence can lead to prices above what would be sustainable in a competitive market.

In 2017, Margrethe Vestager, the European Unions commissioner for competition, said:

It's true that the idea of automated systems getting together and reaching a meeting of minds is still science fiction But we do need to keep a close eye on how algorithms are developing So that when science fiction becomes reality, we're ready to deal with it.

Im not sure were in the realm of science fiction any more.

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AI cartels: what does artificial intelligence mean for competition policy? - Economics Observatory

Artificial Intelligence has had a significant impact on the forex markets in 2023. With artificial intelligence available to nearly every trader, the information floating around the markets is now readily available. Traders can filter the data to help them make trading decisions and quickly find answers to questions that AI provides. Artificial intelligence has also changed the accuracy of the information a trader can request. Vast volumes of data that flow into a trader's workstation can now be analyzed without human intervention.

In many cases, specific criteria are needed, but less programming sophistication is required with AI. Routine tasks that needed human intervention, such as evaluating certain market conditions and executing transactions, can now be replaced by artificial intelligence. Traders can now free up their time to assess trades and use artificial intelligence to handle mundane tasks. AI can also analyze real time data and generate customized trading recommendations based on specific criteria. AI can also help traders conform to correct compliance requirements and legally stick to particular country laws.

Artificial intelligence can also describe and monitor several types of risks. It can help you mitigate your market risks and help you returns your credit risks. With these outputs, AI can also create a trading strategy that will use learned information to generate profits.

Before we jump into why artificial intelligence impacts forex trading, it will be helpful to define artificial intelligence. AI is the ability of a computer to think and learn. It is a branch of computer science that tracks intelligent behavior by using simulations. AI is used to create intelligent machines that can think, reason, learn, and act like humans. AI can solve complex problems, automate processes, and make decisions.

AI is constructed using a combination of algorithms, data, and computing power. Algorithms are used to process data and create models that can be used to make predictions or decisions. Data is used to train the algorithms and to provide input to the models. Computing power is used to run the algorithms and models.

AI information can be calculated on various platforms, including cloud computing, local servers, and mobile devices.

Information Artificial intelligence

Artificial intelligence can filter information using natural language processing (NLP) algorithms to identify and classify relevant information. This process can be done by training the AI system to recognize specific keywords or phrases or by using machine learning algorithms to identify patterns in the data. AI can also detect and remove spam or malicious content from a dataset.

For example, if you want to focus on the dollar, you can train an AI to use NLP to find all the information discussed on the dollar. If you're going to filter down, you can make it the dollar and the Euro. Further, you can find criteria that describe an exchange rate point and make observations based on this information.

Realtime Trading Analysis

Realtime AI trading analysis uses artificial intelligence (AI) to analyze and make decisions about stock market trades in real time. AI trading analysis can identify trends, predict market movements, and decide when to buy and sell currency pairs and any other type of asset. AI trading analysis can also automate trading decisions, allowing traders to make decisions faster and more accurately.

Handling Compiance

Handling compliance is another task that can be accomplished using artificial intelligence. Compliance in trading is adhering to laws, regulations, and standards governingsecurities trading. Compliance is essential for traders to ensure that their trading activities are conducted in a manner that is consistent with the applicable laws and regulations. Compliance also helps to protect investors from fraud and other unethical practices.

AI can help with trading compliance by providing automated monitoring and surveillance of trading activities. AI can detect suspicious patterns and alert compliance officers to potential violations. AI can also analyze large amounts of data quickly and accurately, allowing compliance officers to identify potential issues more rapidly and efficiently. AI can also automate the process of filing and tracking compliance reports, reducing the amount of manual labor required. Finally, AI can provide predictive analytics, allowing compliance officers to anticipate potential issues before they arise.

Handling Legal Issues

AI can help with legal trading issues by providing automated analysis of legal documents, contracts, and other legal documents. AI can also help with legal research, providing insights into legal precedents and helping to identify potential legal risks. AI can also help with compliance, providing automated monitoring of trading activities and alerting traders to potential legal issues. Finally, AI can help with dispute resolution, providing automated analysis of legal arguments and helping to identify potential solutions.

For example, AI can assist in handling a breach of contract when one party fails to fulfill its obligations under an agreement.AI and can recognize market manipulation, which is the intentional distortion of market prices. Artificial intelligence can also acknowledge money Laundering using specific criteria, disguising the source of illegally obtained money.

AI Can Handle Market and Credit Risk

AI can also help you optimize your market risks. Market risk is the risk of investment losses due to changes in market prices. It is the risk that an investor will experience losses due to factors that affect the overall performance of the financial markets in which they are invested. This situation includes interest rate changes, currency fluctuations, and economic and political events.

AI can help manage market risks in several ways. AI can identify patterns in market data, such as price movements, volume, and other indicators, to help predict future market trends. AI can also be used to develop automated trading strategies that can help reduce risk by taking advantage of market opportunities. AI can also create algorithms to help identify and manage portfolio risk, such as diversifying investments and rebalancing portfolios. Finally, AI can be used to develop risk management systems that can help identify and address potential risks in real time.

Ai can help with credit risks by using predictive analytics to identify patterns in customer data that can help lenders better assess the risk of a loan. AI can also help lenders identify potential fraud and other risks associated with a trading margin. AI can also help lenders automate the process of assessing creditworthiness, which can help reduce the time and cost associated with manual credit risk assessment.

How Does AI Help Report Trading Margin

AI can help with trading margins by providing automated trading strategies that can be used to identify and capitalize on profitable trading opportunities.. AI can also be used to automate the process of margin trading, allowing traders to quickly and accurately execute trades with minimal manual intervention.

Margin in trading is the amount of money a trader must deposit to open a position. It is the difference between the total value of the position and the amount of money the trader has to put up. Margin is used to cover potential losses that may occur in the position. When you use a margin account, you are posting collateral.

Collateral is an asset that a trader pledges to a lender to secure the ability to use margin. The collateral is used to secure the margin if the borrower defaults. Collateral can be in the form of cash or an underlying asset that can be liquidated quickly.

Equity is the total value of a trader's account, which includes any unrealized profits or losses from open positions and any funds deposited into the account. Equity is the basis for determining margin requirements and can also be used to calculate a trader's profit or loss.

The Bottom Line

AI can help with forex in a variety of ways. AI can analyze large amounts of data quickly and accurately, identify patterns and trends in the market, and make predictions about future price movements. AI can also systematically create a trading method, allowing traders to analyze market opportunities without manually monitoring the market. AI can also be used to develop and backtest trading strategies, helping traders to identify profitable strategies and minimize risk.

Backtesting is testing a trading strategy on historical data to determine its viability before putting it into practice in realtime trading. It involves simulating the system on historical data and analyzing the results to see if it would have been profitable. Backtesting can help traders identify potential flaws in their strategies and make adjustments before risking real money.

Artificial intelligence can help you streamline your retail trading account or business. Many tasks that need to be routinely done, such as confirming trades, running a profit and loss, and evaluating your margin, can be accomplished using artificial intelligence. By eliminating some of the task work associated with running a trading business, AI can give you more time to focus on trading strategies and risk management.

Moneycontrol Journalists were not involved in the creation of the article.

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How is Artificial Intelligence Impacting the Forex Markets - Moneycontrol