Artificial intelligence (AI) has gained massive popularity over the past year. There is huge interest in AI stocks and every investor is looking for an opportunity to double their money. Nvidia(NASDAQ:NVDA) is a leader in the space and has already made investors rich but many under-the-radar AI stocks can outshine Nvidia.

The global AI market is expected to grow at a 15.83% compound annual growth rate by 2030 and reach amarket volume of $738.80 billion. This means investors have a massive opportunity to benefit from the rising adoption of AI. Nvidia is an expensive stock today, but if you are looking for reasonably priced under-the-radar AI stocks, here are the emerging leaders who could become hot property in the coming months.

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At one time, Palantir(NYSE:PLTR) wasnt an investor favorite due to its secrecy. However, the company has gained massive popularity with its AI prowess and artificial intelligence platform (AIP), which is a big hit among companies.

It held boot camps to enable firms to understand how AIP works and there is a massive backlog of boot camps, showing the growing popularity of Palantirs AI product. Up 190% in the year and 48% year-to-date, PLTR stock is trading at $24 and looks undervalued to me.

The U.S. army has chosen Palantir to builda next-generation targeting systemwith a $178 million deal. It already has a strong presence in the defense industry, and Palantirs solid history of exceptional performance makes it an obvious choice for government contracts. However, it has also grown the commercial business by32% year-over-yearin the fourth quarter.

The AI industry is growing, and Palantir could be the next Nvidia. Analysts love the stock and have a positive rating. Wedbush analyst Dan Ives has a price target of $35 and calls it the Lionel Messi of AI. Further,Brian Stutland, a portfolio manager with Equity Armor Investments, considers Palantir a promising AI investment with a price target of $37, a 57% rise from the current level.

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Investors have constantly compared Nvidia and Advanced Micro Devices(NASDAQ:AMD). Nvidia has moved ahead in the race, but AMD isnt the one to sit back and watch. The stock has been on a tear since 2023 but is still up 29% YTD and 87% in the year.

Trading at $179, the stock is a solid buy before it starts to soar higher. While you might not see a rally or explosive gains, it will steadily progress. Investors have begun to give up on the stock, but it is too soon. AMD has the potential to bounce back.

The company has launched MI300X, which it believes is the worlds fastest AI hardware, and some of the biggest organizations in the world use its GPUs. Its fourth-quarter results were proof that the company is gaining strength. It saw a 10% YOY rise in sales and a 62% surge in personal computer sales in the quarter.

Several companies in the industry are looking for low-cost alternatives, and this is where AMD chips can win over Nvidia. The PC market is anticipated to improve in the coming months as the demand for AI-enabled PCs will rise. This could work as a catalyst for AMD stock.

KeyBanc analysthas an overweight rating for the stock with a price target of $270, while Melius Researchanalyst has a price target of $265 with a buy rating.

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ServiceNow(NYSE:NOW) stock is unstoppable, soaring 71% in the year and 11% YTD. Trading at $767, the stock isnt cheap, but it is on its way to becoming the next Nvidia. The company offers solutions to organizations to handle their workforce and customer experience.

It uses AI and workflow management tools that help identify bottlenecks that are impacting the completion of tasks. The company has already signed a partnership with Nvidia to develop AI solutions.

ServiceNow will adopt the latest AI systems of Nvidia to enhance its platform, and Nvidia will expand the services offered by ServiceNow. This means the company will continue to benefit as long as Nvidia keeps growing.

Fundamentally, the company has impressed investors and reported an EPS of $3.11 in the fourth quarter. It beat analyst expectations with a revenue of $2.4 million, up 26% YOY. One of the most important metrics, the subscription revenue, was up 27% in the quarter to hit $2.3 billion, and it closed 168 deals worth $1 million or more in the quarter. This is higher than the 103 deals closed by Palantir.

ServiceNow is trading at a premium, but I believe the first-quarter results will be impressive and could drive the stock higher. There is no stopping the stocks rally.

On the date of publication, Vandita Jadeja did not hold (either directly or indirectly) any positions in the securities mentioned in this article. The opinions expressed in this article are those of the writer, subject to the InvestorPlace.comPublishing Guidelines.

Vandita Jadeja is a CPA and a freelance financial copywriter who loves to read and write about stocks. She believes in buying and holding for long term gains. Her knowledge of words and numbers helps her write clear stock analysis.

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3 Under-the-Radar AI Stocks That Could Outshine Nvidia - InvestorPlace

This research complied with all relevant ethical regulations. The study that produced the AI assistance dataset29 used in this study was determined by the Massachusetts Institute of Technology (MIT) Committee on the Use of Humans as Experimental Subjects to be exempt through exempt determination E-2953.

This study used 324 retrospective patient cases from Stanford Universitys healthcare system containing chest X-rays and clinical histories, which include patients indication, vitals and labs. In this study, we analyzed data collected from a total of 140 radiologists participating in two experiment designs. The non-repeated-measure design included 107 radiologists in a non-repeated-measure setup (Supplementary Fig. 1). Each radiologist read 60 patient cases across four subsequences that each contained 15 cases. Each subsequence corresponded to one of four treatment conditions: with AI assistance and clinical histories, with AI assistance and without clinical history, without AI assistance and with clinical histories and without AI assistance and clinical histories. The four subsequences and associated treatment conditions were organized in a random order. The 60 patient cases were randomly selected and randomly assigned to one of the treatment conditions. This design included across-subject and within-subject variations in the treatment conditions; it did not allow within-case-subject comparisons because a case was encountered only once for a radiologist38. Order effects were mitigated by the randomization of treatment conditions. The repeated-measure design included 33 radiologists in a repeated-measure setup (Supplementary Fig. 2). Each radiologist read a total of 60 patient cases, each under each of the four treatment conditions and producing a total of 240 diagnoses. The radiologist completed the experiment in four sessions, and the radiologist read the same 60 randomly selected patient cases in each session under each of the various treatment arms. In each session, 15 cases were read in each treatment arm in batches of five cases. Treatments were randomly ordered. This resulted in the radiologist reading each patient case under a different treatment condition over the four sessions. There was a 2-week washout period15,39,40 between every session to minimize order effects of radiologists reading the same case multiple times. This design included across-subject and within-subject variations as well as across-case-radiologist and within-case-radiologist variations in treatment conditions. Order effects were mitigated by the randomization of treatment conditions. No enrichment was applied to the data collection process. We combined data from both experiment designs from the clinical history conditions. Further details about the data collection process are available in a separate study29, which focuses on establishing a Bayesian framework for defining optimal humanAI collaboration and characterizing actual radiologist behavior in incorporating AI assistance. The study was determined exempt by the MIT Committee on the Use of Humans as Experimental Subjects through exempt determination E-2953.

There are 15 pathologies with corresponding AI predictions: abnormal, airspace opacity, atelectasis, bacterial/lobar pneumonia, cardiomediastinal abnormality, cardiomegaly, consolidation, edema, lesion, pleural effusion, pleural other, pneumothorax, rib fracture, shoulder fracture and support device hardware. These pathologies, the interrelations among these pathologies and additional pathologies without AI predictions can be visualized in a hierarchical structure in Supplementary Fig. B.1. Radiologists were asked to familiarize themselves with the hierarchy before starting, had access to the figure throughout the experiment and had to provide predictions for pathologies following this hierarchy. This aimed to maximize clarity on the specific pathologies referenced in the experiment. When radiologists received AI assistance, they were simultaneously presented with the AI predictions for these 15 pathologies along with the patients chest X-ray and, if applicable, their clinical history. The AI predictions were presented in the form of prediction probabilities on a 0100 scale. The AI predictions were generated by the CheXpert model8, which is a DenseNet121 (ref. 41)-based model for chest X-rays that has been shown to perform similarly to board-certified radiologists. The model generated a single prediction for fracture that was used as the AI prediction for both rib fracture and shoulder fracture. Authors of the CheXpert model8 decided on the 14 pathologies (with a single prediction for fracture) based on the prevalence of observations in radiology reports in the CheXpert dataset and clinical relevance, conforming to the Fleischner Societys recommended glossary42 whenever applicable. Among the pathologies, they included Pneumonia (corresponding to bacterial/lobar pneumonia) to indicate the diagnosis of primary infection and No Finding (corresponding to abnormal) to indicate the absence of all pathologies. These pathologies were set in the creation of the CheXpert labeler8, which has been applied to generate labels for reports in the CheXpert dataset and MIMIC-CXR43, which are among the largest chest X-ray datasets publicly available.

The ground truth probabilities for a patient case were determined by averaging the continuous predicted probabilities of five board-certified radiologists from Mount Sinai Hospital with at least 10years of experience and chest radiology as a subspecialty on a 0100 scale. For instance, if the predicted probabilities of the five board-certified radiologists are 91, 92, 92, 100 and 100, respectively, the ground truth probability is 95. The prevalence of the pathologies based on a ground truth probability threshold of 50 of a pathology being present is shown in Supplementary Table 1.

The participating radiologists represent a diverse set of institutions recruited through two means. Their primary affiliations include large, medium and small clinical settings and non-clinical settings. Additionally, some radiologists are affiliated with an academic hospital, whereas others are not. Radiologists in the non-repeated-measure design were recruited from teleradiology companies. Radiologists in the repeated-measure design were recruited from the Vinmec health system in Vietnam. Details about the participating radiologists and recruitment process can be found in Supplementary Note | Participant recruitment and affiliation.

The experiment interface and instructions presented to participating radiologists can be found in Supplementary Note | Experiment interface and instructions. Before entering the experiment, radiologists were instructed to walk through the experiment instructions, the hierarchy of pathological findings, basic information and performance of the AI model, video demonstration of the experiment interface and examples, consent clauses, comprehension check questions, information on bonus payment that incentivizes effort and practice patient cases covering four treatment conditions and showing example AI predictions from the AI model used in the experiment.

Sex and gender statistics of the participating radiologists and patient cases are available in Supplementary Tables 39 and 40, respectively. Sex and gender were not considered in the original data collection procedures. Disaggregated information about sex and gender at the individual level was collected in the separate study and will be made available29.

We used the empirical Bayes method30 to shrink the raw mean heterogeneous treatment effects and performance metrics of individual radiologists measured on the dataset toward the grand mean to ameliorate overestimating heterogeneity due to sampling error. The values include AIs treatment effects on error, sensitivity and specificity and performance metrics on unassisted error, sensitivity and specificity.

Assume that ({t}_{r}) is radiologist rs true mean treatment effect from AI assistance or any metric of interest. We observe

$$tilde{t}_{r}={t}_{r}+{{{eta }}}_{r}$$

(1)

which differs from ({t}_{r}) by ({{{eta }}}_{r}). We use a normal distribution as the prior distribution over the metric of interest. The mean of the prior distribution can be computed as

$$Eleft[tilde{t}_{r}right]=Eleft[{t}_{r}right],$$

(2)

the mean of the observed mean metric of interest of radiologists. The variance of the prior distribution can be computed as

$$Eleft[{Big({t}_{r}-Eleft[{t}_{r}right]Big)}^{2}right]=Eleft[{left(tilde{t}_{r}-Eleft[tilde{t}_{r}right]right)}^{2}right]-Eleft[{{{eta }}}_{r}^{2}right],$$

(3)

the variance of the observed mean metric of interest of radiologists minus the estimated (Eleft[{{{eta }}}_{r}^{2}right]). We can estimate (Eleft[{{{eta }}}_{r}^{2}right]) with

$$Eleft[{{{eta }}}_{r}^{2}right]=Eleft[{left(frac{1}{{N}_{r}}mathop{sum }limits_{i}{t}_{{ir}}-Eleft[{t}_{{ir}}right]right)}^{2}right]=Eleft[frac{{sum }_{i}{left({t}_{{ir}}-Eleft[{t}_{{ir}}right]right)}^{2}}{{N}_{r}}right]=Eleft[s.e.{left(tilde{t}_{r}right)}^{2}right].$$

(4)

Denote the estimated mean and variance of the prior distribution as ({{rm{mu }}}_{0}) and ({{rm{sigma }}}_{0}^{2}). We can compute the mean of the posterior distribution for radiologist (r) as

$$frac{{{rm{sigma }}}_{r}^{2}{{rm{mu }}}_{0}+{{rm{sigma }}}_{0}^{2}{{rm{mu }}}_{r}}{{{rm{sigma }}}_{0}^{2}+{{rm{sigma }}}_{r}^{2}}$$

(5)

where ({{rm{mu }}}_{r}=widetilde{{t}}_{t}) and ({{rm{sigma }}}_{r}=s.e.left(widetilde{{t}}_{r}right)); we can compute the variance of the posterior as

$$frac{{{rm{sigma }}}_{0}^{2}{{rm{sigma }}}_{r}^{2}}{{{rm{sigma }}}_{0}^{2}+{{rm{sigma }}}_{r}^{2}}$$

(6)

where ({{rm{sigma }}}_{r}=s.e.left(widetilde{{t}}_{r}right)). The updated mean of the posterior distribution is the radiologists metric of interest after shrinkage.

For the analysis on treatment effects on absolute error, we focus on high-prevalence pathologies with prevalence greater than 10%, because radiologists baseline performance without AI assistance is generally highly accurate on low-prevalence pathologies, where they correctly predict that a pathology is not present, and, as a result, there is little variation in radiologists errors. This is especially true when computing each individual radiologists treatment effect. When there is zero variance in the performance of a radiologist under a treatment condition, the associated standard error estimate is zero, making it impossible to perform inference on this radiologists treatment effect.

The combined characteristics model was fitted on a training set of half of the radiologists (n=68) to predict treatment effects of the test set of the remaining half (n=68). The treatment effect predictions on the test set were used as the combined characteristics score for splitting the test set radiologists into binary subgroups (based on whether a particular radiologists combined characteristics score was smaller than or equal to the median treatment effect of radiologists computed from all available reads). Then, the same procedure was repeated after flipping the training set and test set radiologists to split the other set of radiologists into binary subgroups. The experience-based characteristics of radiologists in the randomly split training set and test set were balanced: one set contained 27 radiologists with less than or equal to 6years of experience and 41 radiologists with more than 6years of experience, and the other set contained 41 and 27, respectively. One set contained 47 radiologists who did not specialize in thoracic radiology and 21 radiologists who did, and the other set contained 54 and 14 radiologists, respectively. One set contained 32 radiologists without experience with AI tools and 36 radiologists with experience, and the other set contained 31 and 37, respectively.

To compute a radiologists observed mean treatment effect and the corresponding standard errors and the overall treatment effect of AI assistance across subgroups, we built a linear regression model with the following formulation using the statsmodels library: error1+C(treatment). Here, error refers to the absolute error of a radiologist prediction; 1 refers to an intercept term; and treatment refers to a binary indicator of whether the prediction is made with or without AI assistance. This formulation allows us to compute the treatment effect of AI assistance for both non-repeated-measure and repeated-measure data.

For the analyses on experience-based radiologist characteristics and AI error, we computed the treatment effects of subgroups split based on the predictor of interest by building a linear regression model with the following formulation using the statsmodels library: error1+C(subgroup)+C(treatment):C(subgroup). Here, error refers to the absolute error of a radiologist prediction; 1 refers to an intercept term; subgroup refers to an indicator of the subgroup that the radiologist is split into; and treatment refers to a binary indicator of whether the prediction is made with or without AI assistance. This formulation allows us to compute the subgroup-specific treatment effect of AI assistance for both non-repeated-measure data and repeated-measure data.

To account for correlations of observations within patient cases and radiologists, we computed cluster-robust standard errors that are two-way clustered at the patient case and radiologist level for all inferences unless otherwise specified44,45. With the statsmodels librarys ordinary least squares (OLS) class, we used a clustered covariance estimator as the type of robust sandwich estimator and defined two-way groups based on identifiers of the patient cases and radiologists. The approach assumes that regression model errors are independent across clusters defined by the patient cases and radiologists and adjusts for correlations within clusters.

The reversion to the mean effect and the mechanism of split sampling in avoiding reversion to the mean are explained in the following derivation:

Suppose that ({u}_{i,r}^{* }) and ({a}_{i,r}^{* }) are the true unassisted and assisted diagnostic error of radiologist (r) on patient case i. Suppose that we measure ({u}_{i,r}={u}_{i,r}^{* }+{e}_{i,r}^{u}) and ({a}_{i,r}={a}_{i,r}^{* }+{e}_{i,r}^{a}) where ({e}_{i,r}^{u}) and ({e}_{i,r}^{a}) are measurement errors. Assume that the measurement errors are independent of ({u}_{i,r}^{* }) and ({a}_{i,r}^{* }).

To study the relationship between unassisted error and treatment effect, we intend to build the following linear regression model:

$${u}_{r}^{* }-{a}_{r}^{* }={{beta }}{u}_{r}^{* }+{e}_{r}^{* }$$

(7)

where the error is independent of the independent variable, and ({u}_{r}^{* }) and ({a}_{r}^{* }) are the mean unassisted and assisted performance of radiologist (r). Here, the moment condition

$$Eleft[{e}_{i,r}^{* }times {u}_{i,r}^{* }right]=0$$

(8)

is as desired. This univariate regression estimates the true value of ({{beta }}), which is defined as

$$frac{{rm{Cov}}({{rm{u}}}_{{rm{r}}}^{ast }-{{rm{a}}}_{{rm{r}}}^{ast },,{{rm{u}}}_{{rm{r}}}^{ast })}{{rm{Var}}({{rm{u}}}_{{rm{r}}}^{ast })}$$

(9)

However, because we have access only to noisy measurements ({u}_{r}) and ({a}_{r}), consider instead an approach that builds the model

$${u}_{r}-{a}_{r}={{beta }}{u}_{r}+{e}_{r}$$

(10)

and assumes the moment condition

$$Eleft[{e}_{r}times {u}_{r}right]=0.$$

(11)

This linear regression model using noisy measurements instead generates the following estimate of ({{beta }}):

$$frac{{Cov}left({u}_{r}-{a}_{r},{u}_{r}right)}{{Var}left({u}_{r}right)}=frac{{Cov}left({u}_{r}^{* }-{a}_{r}^{* },{u}_{r}^{* }right)+{Var}left({e}_{r}^{u}right)}{{Var}left({u}_{r}^{* }right)+{Var}left({e}_{r}^{u}right)}$$

(12)

which is incorrect because of the additional ({{V}},{{ar}}left({{{e}}}_{{{r}}}^{{{u}}}right)) terms in the numerator and the denominator. The additional term in the denominator represents attenuation bias, which we address in detail in a later subsection. The term in the numerator represents the reversion to the mean issue, which we now discuss in further detail.

As the equation shows, the bias caused by reversion to the mean is positive. This term exists because the moment condition (Eleft[{e}_{r}times {u}_{r}right]=0), equation (11), is not valid at the true value of ({{beta }}) as shown in the following derivation:

$$begin{array}{c}Eleft[left({u}_{r}-{a}_{r}-{{beta }}{u}_{r}right)times {u}_{r}right]=Eleft[left(left(1-{{beta }}right){u}_{r}-{a}_{r}right)times {u}_{r}right]\ begin{array}{c}=Eleft[left(left(1-{{beta }}right)left({u}_{r}^{* }+{e}_{r}^{u}right)-left({a}_{r}^{* }+{e}_{r}^{a}right)right)times {u}_{r}right]\ begin{array}{c}=Eleft[left(left(left(1-{{beta }}right){u}_{r}^{* }-{a}_{r}^{* }right)+left(1-{{beta }}right){e}_{r}^{u}-{e}_{r}^{a}right)times {u}_{r}right]\ begin{array}{c}=Eleft[left({e}_{r}^{* }+left(1-{{beta }}right){e}_{r}^{u}-{e}_{r}^{a}right)times {u}_{r}right]\ begin{array}{c}=left(1-{{beta }}right)Eleft[{e}_{r}^{u}times {u}_{r}right]\ =left(1-{{beta }}right){Var}left({e}_{r}^{u}right)ne 0.end{array}end{array}end{array}end{array}end{array}$$

Split sampling solves this bias by using separate patient cases for computing unassisted error and treatment effect. A simple construction of split sampling is to use a separate case i for computing the treatment effect and using the remaining cases to compute unassisted error. With this construction, we obtain the following estimate of ({{beta }}):

$$frac{{Cov}left({u}_{i,r}-{a}_{i,r},{u}_{ne i,r}right)}{{Var}left({u}_{ne i,r}right)}$$

(13)

where ({u}_{i,r}) is the unassisted performance on case i for radiologist (r), and ({u}_{ne i,r}) is the mean unassisted performance computed on all unassisted cases other than i. If the errors on each case used to compute ({u}_{r}^{* }) and ({a}_{r}^{* }) are independent, the estimate of ({{beta }}) is equal to

$$frac{{Cov}left({u}_{r}^{* }-{a}_{r}^{* },{u}_{r}^{* }right)}{{Var}left({u}_{ne i,r}right)}$$

(14)

The remaining discrepancy in the denominator again represents attenuation bias and is addressed in a later subsection.

To study unassisted error as a predictor of treatment effect, we built a linear regression model with the following formulation using the statsmodels library: treatment effect1+unassisted error. We designed the following split sampling construction to maximize data efficiency when computing the independent and dependent variables in the linear regression.

Let i index a patient case and (r) index a radiologist. Assume that a radiologist reads ({N}_{u}) cases unassisted and ({N}_{a}) cases assisted. Recall that the unassisted and assisted cases are disjoint for the non-repeated-measure data; they overlap exactly for the repeated-measure data.

For the non-repeated-measure design, we adopt the following construction:

$${u}_{i,r}-{a}_{r}={{beta }}{x}_{ne i,r}+{{rm{varepsilon }}}_{{u}_{i,r}}+{{rm{varepsilon }}}_{{a}_{r}}$$

(15)

where ({x}_{ne i,r}=frac{1}{{N}_{u}-1}{sum }_{kne i}{u}_{k,r}) and ({a}_{r}=frac{1}{{N}_{a}}{sum }_{k}{a}_{k,r}). Here, ({x}_{ne i,r}) is the mean unassisted performance computed on all unassisted cases other than i; ({u}_{{i},{r}}) is the unassisted performance on case i for radiologist (r); and ({a}_{r}) is the mean assisted performance on all assisted cases for radiologist (r).

For the repeated-measure design, we adopt the following construction:

$${u}_{i,r}-{a}_{i,r}={{beta }}{x}_{ne i,r}+{{rm{varepsilon }}}_{{u}_{i,r}}+{{rm{varepsilon }}}_{{a}_{i,r}}$$

(16)

where ({x}_{ne i,r}=frac{1}{{N}_{u}-1}{sum }_{kne i}{u}_{k,r}). Here, ({x}_{ne i,r}) is the mean unassisted performance computed on all cases other than i; ({u}_{i,r}) is the unassisted performance on case i for radiologist (r); and ({a}_{i,r}) is the assisted performance on case i for radiologist (r).

To study unassisted error as a predictor of assisted error, we built a linear regression model with the following formulation using the statsmodels library: assisted error1+unassisted error. We designed the following split sampling construction that maximizes data efficiency when computing the independent and dependent variables in the linear regression.

For the non-repeated-measure design, we adopt the following construction:

$${a}_{i,r}={{beta }}{x}_{r}+{{rm{varepsilon }}}_{i,r}$$

(17)

where ({x}_{r}=frac{1}{{N}_{u}}{sum }_{k},{x}_{k,r}). Here, ({x}_{r}) is the mean unassisted performance computed on all unassisted cases, and ({a}_{i,r}) is the assisted performance on case i for radiologist (r).

For the repeated-measure design, we adopt the following construction:

$${a}_{i,r}={{beta }}{x}_{ne i,r}+{{rm{varepsilon }}}_{i,r}$$

(18)

where ({x}_{ne i,r}=frac{1}{{N}_{u}-1}{sum }_{kne i}{u}_{k,r}). Here, ({x}_{ne i,r}) is the mean unassisted performance computed on all unassisted cases other than i and ({a}_{i,r}) is the assisted performance on case i for radiologist (r).

The constructions above again emphasize the necessity for split sampling. Without split sampling, the mean unassisted performance, which is the independent variable of the linear regression, will be correlated with the error terms due to overlapping patient cases, leading to a bias in the regression.

We adjusted for attenuation bias for the split sampling linear regression formulations.

We want to estimate regressions of the form

$${Y}_{r}={{{beta }}}_{0}+{{{beta }}}_{1}Eleft[{x}_{r}right]+{{rm{varepsilon }}}_{r}$$

(19)

where ({Y}_{r}) is an outcome for radiologist (r) and (Eleft[{x}_{r}right]) is radiologist (r)s average unassisted performance. We observe

$$widetilde{{x}}_{r}=frac{1}{{N}_{r}}mathop{sum }limits_{i}{x}_{{ir}}=Eleft[{x}_{r}right]+{{{eta }}}_{r}$$

(20)

where ({{{eta }}}_{r}=frac{1}{{N}_{r}}mathop{sum }limits_{i}{x}_{{ir}}-Eleft[{x}_{r}right]) and (Eleft[{{{eta }}}_{r}{x}_{r}right]=0) and (Eleft[{{{eta }}}_{r}{{rm{varepsilon }}}_{r}right]=0), which are justified by independent and identically distributed (i.i.d.) sampling of cases and split sampling, respectively.

Using observations from the experiment, we estimate the following regression:

$${Y}_{r}={{rm{gamma }}}_{0}+{{rm{gamma }}}_{1}tilde{x}_{r}+{{rm{varepsilon }}}_{r}$$

(21)

Recall that

$$begin{array}{rcl}{{hat{rm{gamma }}}_{1}}{to }^{p}frac{Eleft[left({x}_{r}+{{{eta }}}_{r}-Eleft[{x}_{r}right]right)left({Y}_{r}-Eleft[{Y}_{r}right]right)right]}{Eleft[{left({x}_{r}+{{{eta }}}_{r}-Eleft[{x}_{r}right]right)}^{2}right]} =\ frac{Eleft[left({x}_{r}-Eleft[{x}_{r}right]right)left({Y}_{r}-Eleft[{Y}_{r}right]right)right]}{Eleft[{left({x}_{r}-Eleft[{x}_{r}right]right)}^{2}right]+Eleft[{{{eta }}}_{r}^{2}right]}={{{beta }}}_{1}{rm{lambda }}end{array}$$

(22)

where ({rm{lambda }}=frac{Eleft[{left({x}_{r}-Eleft[{x}_{r}right]right)}^{2}right]}{Eleft[{left({x}_{r}-Eleft[{x}_{r}right]right)}^{2}right]+Eleft[{{{eta }}}_{r}^{2}right]}) and ({{{beta }}}_{1}=frac{Eleft[left({x}_{r}-Eleft[{x}_{r}right]right)left({Y}_{r}-Eleft[{Y}_{r}right]right)right]}{Eleft[{left({x}_{r}-Eleft[{x}_{r}right]right)}^{2}right]}). We can estimate ({rm{lambda }}) using a plug-in estimator for each term in the data: (1)

$$begin{array}{rcl}Eleft[{{{eta }}}_{r}^{2}right]=Eleft[{left(frac{1}{{N}_{r}}mathop{sum }limits_{i}{x}_{{ir}}-Eleft[{x}_{{ir}}right]right)}^{2}right]\=Eleft[frac{{sum }_{i}{left({x}_{{ir}}-Eleft[{x}_{{ir}}right]right)}^{2}}{{N}_{r}}right]=Eleft[s.e.{left(tilde{x}_{r}right)}^{2}right].end{array}$$

(23)

This is the standard error of the mean estimator. (2)

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Heterogeneity and predictors of the effects of AI assistance on radiologists - Nature.com

Key Takeaways

Microsoft Corp. (MSFT) continued to point the company toward a generative artificial intelligence (AI) future with the launch Thursday of its first business-focused Surface PCs. Here are the new features you can expect to find in the Surface Pro 10 for Business and Surface Laptop 6 for Business.

The new Surface PCs are driven by Intel Corp. (INTC) Core ultra processors designed to provide powerful and reliable performance for business applications. Microsoft said its Surface Laptop 6 is two times faster than Laptop 5, while the Surface Pro 10 is up to 53% faster than the Pro 9. The enhanced speed and Neural Processing Unit (NPU) technology allow users to benefit from AI tools such as Windows Studio Effects and give business users and developers an opportunity to build their own AI apps and experiences.

Microsoft said the Surface Pro 10 for Business is its most powerful model to date and includes a new Copilot key. The new addition to the Windows keyboard will allow shortcut access to the company's flagship Copilot AI tool. Other improvements to the keyboard include a bold keyset, larger font size, and backlighting to make typing easier, alongside a screen that is 33% brighter, according to the company. Microsoft 365 apps like OneNote and Copilot also will be able to use AI to analyze handwritten notes on the Surface Slim Pen.

For the Surface Pro 10, Microsoft has focused much of its upgrade on an enhanced video calling experience. A new Ultrawide Studio Camera is its best front-facing camera on a Windows 2-in-1 or laptop that features a 114 field of view, captures video in 1440 pixels, and uses AI-powered Windows Studio Effects to ensure presentation quality, Microsoft said. The company also has launched a series of new accessories for users who want an alternative to the traditional mouse. These include custom grips on the Surface Pen and an adaptive hub device that offers three USB ports.

Finally, the new Surface PCs for business have added security features for business users, which include smart card reader technology. Surface users can access the PC with "chip-to-cloud" ID card security for authentication. Surface 10 users can get access to new near-field communication (NFC) reader technology that allows for secure, password-less authentication with NFC security keys.

Microsoft will host a special Windows and Surface AI event on May 20, at which Chief Executive Officer (CEO) Satya Nadella will outline the company's "AI vision for software and hardware. Earlier this week, the company announced that it had hired DeepMind co-founder Mustafa Suleyman as the CEO of its growing AI unit.

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Microsoft's First AI Surface PC: What Does It Offer? - Investopedia

Enlarge / The United Nations building in New York.

On Thursday, the United Nations General Assembly unanimously consented to adopt what some call the first global resolution on AI, reports Reuters. The resolution aims to foster the protection of personal data, enhance privacy policies, ensure close monitoring of AI for potential risks, and uphold human rights. It emerged from a proposal by the United States and received backing from China and 121 other countries.

Being a nonbinding agreement and thus effectively toothless, the resolution seems broadly popular in the AI industry. On X, Microsoft Vice Chair and President Brad Smith wrote, "We fully support the @UN's adoption of the comprehensive AI resolution. The consensus reached today marks a critical step towards establishing international guardrails for the ethical and sustainable development of AI, ensuring this technology serves the needs of everyone."

The resolution, titled "Seizing the opportunities of safe, secure and trustworthy artificial intelligence systems for sustainable development," resulted from three months of negotiation, and the stakeholders involved seem pleased at the level of international cooperation. "We're sailing in choppy waters with the fast-changing technology, which means that it's more important than ever to steer by the light of our values," one senior US administration official told Reuters, highlighting the significance of this "first-ever truly global consensus document on AI."

In the UN, adoption by consensus means that all members agree to adopt the resolution without a vote. "Consensus is reached when all Member States agree on a text, but it does not mean that they all agree on every element of a draft document," writes the UN in a FAQ found online. "They can agree to adopt a draft resolution without a vote, but still have reservations about certain parts of the text."

The initiative joins a series of efforts by governments worldwide to influence the trajectory of AI development following the launch of ChatGPT and GPT-4, and the enormous hype raised by certain members of the tech industry in a public worldwide campaign waged last year. Critics fear that AI may undermine democratic processes, amplify fraudulent activities, or contribute to significant job displacement, among other issues. The resolution seeks to address the dangers associated with the irresponsible or malicious application of AI systems, which the UN says could jeopardize human rights and fundamental freedoms.

Resistance from nations such as Russia and China was anticipated, and US officials acknowledged the presence of lots of heated conversations during the negotiation process, according to Reuters. However, they also emphasized successful engagement with these countries and others typically at odds with the US on various issues, agreeing on a draft resolution that sought to maintain a delicate balance between promoting development and safeguarding human rights.

The new UN agreement may be the first "global" agreement, in the sense of having the participation of every UN country, but it wasn't the first multi-state international AI agreement. That honor seems to fall to the Bletchley Declaration signed in November by the 28 nations attending the UK's first AI Summit.

Also in November, the US, Britain, and other nations unveiled an agreement focusing on the creation of AI systems that are "secure by design" to protect against misuse by rogue actors. Europe is slowly moving forward with provisional agreements to regulate AI and is close to implementing the world's first comprehensive AI regulations. Meanwhile, the US government still lacks consensus on legislative action related to AI regulation, with the Biden administration advocating for measures to mitigate AI risks while enhancing national security.

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World's first global AI resolution unanimously adopted by United Nations - Ars Technica

The next evolution in artificial intelligence (AI) could lie in agents that can communicate directly and teach each other to perform tasks, research shows.

Scientists have modeled an AI network capable of learning and carrying out tasks solely on the basis of written instructions. This AI then described what it learned to a sister AI, which performed the same task despite having no prior training or experience in doing it.

The first AI communicated to its sister using natural language processing (NLP), the scientists said in their paper published March 18 in the journal Nature.

NLP is a subfield of AI that seeks to recreate human language in computers so machines can understand and reproduce written text or speech naturally. These are built on neural networks, which are collections of machine learning algorithms modeled to replicate the arrangement of neurons in the brain.

Once these tasks had been learned, the network was able to describe them to a second network a copy of the first so that it could reproduce them. To our knowledge, this is the first time that two AIs have been able to talk to each other in a purely linguistic way, said lead author of the paper Alexandre Pouget, leader of the Geneva University Neurocenter, in a statement.

The scientists achieved this transfer of knowledge by starting with an NLP model called "S-Bert," which was pre-trained to understand human language. They connected S-Bert to a smaller neural network centered around interpreting sensory inputs and simulating motor actions in response.

Related: AI-powered humanoid robot can serve you food, stack the dishes and have a conversation with you

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This composite AI a "sensorimotor-recurrent neural network (RNN)" was then trained on a set of 50 psychophysical tasks. These centered on responding to a stimulus like reacting to a light through instructions fed via the S-Bert language model.

Through the embedded language model, the RNN understood full written sentences. This let it perform tasks from natural language instructions, getting them 83% correct on average, despite having never seen any training footage or performed the tasks before.

That understanding was then inverted so the RNN could communicate the results of its sensorimotor learning using linguistic instructions to an identical sibling AI, which carried out the tasks in turn also having never performed them before.

The inspiration for this research came from the way humans learn by following verbal or written instructions to perform tasks even if weve never performed such actions before. This cognitive function separates humans from animals; for example, you need to show a dog something before you can train it to respond to verbal instructions.

While AI-powered chatbots can interpret linguistic instructions to generate an image or text, they cant translate written or verbal instructions into physical actions, let alone explain the instructions to another AI.

However, by simulating the areas of the human brain responsible for language perception, interpretation and instructions-based actions, the researchers created an AI with human-like learning and communication skills.

This won't alone lead to the rise of artificial general intelligence (AGI) where an AI agent can reason just as well as a human and perform tasks in multiple areas. But the researchers noted that AI models like the one they created can help our understanding of how human brains work.

Theres also scope for robots with embedded AI to communicate with each other to learn and carry out tasks. If only one robot received initial instructions, it could be really effective in manufacturing and training other automated industries.

The network we have developed is very small, the researchers explained in the statement. Nothing now stands in the way of developing, on this basis, much more complex networks that would be integrated into humanoid robots capable of understanding us but also of understanding each other.

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Scientists create AI models that can talk to each other and pass on skills with limited human input - Livescience.com