#Data_analysis

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Today, we dispel the myth that artificial intelligence (AI) is overhyped by showing how it is revolutionizing eCommerce. The movie demonstrates how AI-powered systems like Buyist Pro can quickly analyze difficult circumstances, give actionable insights, and ease problem-solving for non-technical people. It uses the scenario of a small business owner dealing with a customer's allegation of a data breach. Artificial intelligence (AI) can interpret natural language questions and provide pertinent, context-aware answers, improving the usability and efficiency of eCommerce systems.

[jessicagonzale]

Today, we dispel the myth that artificial intelligence (AI) is overhyped by showing how it is revolutionizing eCommerce. The movie demonstrates how AI-powered systems like Buyist Pro can quickly analyze difficult circumstances, give actionable insights, and ease problem-solving for non-technical people. It uses the scenario of a small business owner dealing with a customer's allegation of a data breach. Artificial intelligence (AI) can interpret natural language questions and provide pertinent, context-aware answers, improving the usability and efficiency of eCommerce systems.

[tiarrapermata]

Today, we dispel the myth that artificial intelligence (AI) is overhyped by showing how it is revolutionizing eCommerce. The movie demonstrates how AI-powered systems like Buyist Pro can quickly analyze difficult circumstances, give actionable insights, and ease problem-solving for non-technical people. It uses the scenario of a small business owner dealing with a customer's allegation of a data breach. Artificial intelligence (AI) can interpret natural language questions and provide pertinent, context-aware answers, improving the usability and efficiency of eCommerce systems.

[kellescott]

Today, we dispel the myth that artificial intelligence (AI) is overhyped by showing how it is revolutionizing eCommerce. The movie demonstrates how AI-powered systems like Buyist Pro can quickly analyze difficult circumstances, give actionable insights, and ease problem-solving for non-technical people. It uses the scenario of a small business owner dealing with a customer's allegation of a data breach. Artificial intelligence (AI) can interpret natural language questions and provide pertinent, context-aware answers, improving the usability and efficiency of eCommerce systems.

[dorothyklinger]

Today, we dispel the myth that artificial intelligence (AI) is overhyped by showing how it is revolutionizing eCommerce. The movie demonstrates how AI-powered systems like Buyist Pro can quickly analyze difficult circumstances, give actionable insights, and ease problem-solving for non-technical people. It uses the scenario of a small business owner dealing with a customer's allegation of a data breach. Artificial intelligence (AI) can interpret natural language questions and provide pertinent, context-aware answers, improving the usability and efficiency of eCommerce systems.

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[phonemantra-blog]

The update is available for Windows and macOS In 2020, scientists decided to simply rework the alphanumeric characters they used to represent genes, rather than try to figure out the Excel function that interpreted their data as dates and automatically reformatted it. Recently, a member of the Excel team announced that the company is releasing an update for Windows and macOS. Excel's automatic conversions are designed to make entering certain types of frequently entered data, such as numbers and dates, easier and faster. But for scientists, this feature can corrupt input data, as a 2016 study found. [caption id="attachment_72975" align="aligncenter" width="780"] Microsoft Fixes Excel Function[/caption] Microsoft Fixes Excel Function That Was Breaking Scientific Data Microsoft detailed the update on its blog, adding an option in Settings to “Convert continuous letters and numbers to a date.” The update builds on the automatic data conversion settings the company added last year, which included Excel's ability to let you load a file without automatic conversion so you can be sure the feature won't mess anything up. Microsoft's blog has caveats - for example, Excel avoids conversion by storing data as text, which means the data may not work for further calculations. There is also a known issue where conversions cannot be disabled when running macros.

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What's data analysis?

Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. It's like sifting through a pile of sand to find hidden treasures. By analyzing data, we can uncover patterns, trends, and relationships that would be difficult or impossible to see with the naked eye. This information can then be used to make better decisions, solve problems, and improve efficiency.

* https://en.wikipedia.org/wiki/Data_analysis

[omarelfarouk90]

The significance of alcohol consumption and average income on suicide rate

I have performed some data analysis on the data gathered by the gap minder foundation in Stockholm to help in the UN development . i have used the same gathered data to analyze the effect of alcohol consumption and average income on the suicide rate per 100 people, the analysis was conducted using the OLS regression technique from the python stats model.

the input of average income was divided into two categories, greater than or equal $2000 per month and less than $2000 USD per month

the input of the alcohol consumption was divided into two categories according to their consumption per liter

category 0  below 3L per month

category 1 from 3L to 9L consumption per month

the code was as following

Created on Sat Aug  8 10:27:52 2020

@author: omar.elfarouk """

import numpy import pandas as pd import statsmodels.formula.api as smf import statsmodels.stats.multicomp as multi from pandas import DataFrame as df

data = pd.read_csv('gapminder.csv', low_memory=False) df = pd.DataFrame(data) #setting variables you will be working with to numeric df = df.replace(r'\s+', 0, regex=True) #Replace empty strings with zero

#subset data to income per person , alcohol consumption ,suiside rate , and employment sub1=data sub1 = sub1.replace(r'\s+', 0, regex=True) #Replace empty strings with zero #SETTING MISSING DATA

# Creating a secondary variable multiplying income by alcohol consumption by employment rate

#sub1['suicideper100th']=sub1['suicideper100th'].replace(0, numpy.nan)

sub1['suicideper100th']= pd.to_numeric(sub1['suicideper100th'])

#sub1['Income']= pd.to_numeric(sub1['Income']) ct1 = sub1.groupby('suicideper100th').size() print (ct1)

# using ols function for calculating the F-statistic and associated p value model1 = smf.ols(formula='suicideper100th ~ C(Income)', data=sub1).fit() results1 = model1 print (results1.summary())

sub2 = sub1[['suicideper100th', 'Income']].dropna()

print ('means for income by suicide status') m1= sub2.groupby('Income').mean() print (m1)

print ('standard deviations for income suiside status') sd1 = sub2.groupby('Income').std() print (sd1) #i will call it sub3 sub3 = sub1[['suicideper100th', 'Alcoholuse']].dropna()

model2 = smf.ols(formula='suicideper100th ~ C(Alcoholuse)', data=sub3).fit() print (model2.summary())

print ('means for alcohol use by suicide status') m2= sub3.groupby('Alcoholuse').mean() print (m2)

print ('standard deviations for alcohol use by suicide') sd2 = sub3.groupby('Alcoholuse').std() print (sd2) #tuckey honesty test comparision for post hoc test mc1 = multi.MultiComparison(sub3['suicideper100th'], sub3['Alcoholuse']) res1 = mc1.tukeyhsd() print(res1.summary())

the null hypothesis indicates that there is no difference in the level of consumption of alcohol on the suicide rate and also there is no difference in the income level on the suicide rate.

the alternative hypothesis is that there is a significance difference on the alcohol consumption and the average income on the suicide rate.

the results are displayed as following

OLS Regression Results                             ============================================================================== Dep. Variable:        suicideper100th   R-squared:                       0.013 Model:                            OLS   Adj. R-squared:                  0.009 Method:                 Least Squares   F-statistic:                     2.875 Date:                Sun, 09 Aug 2020   Prob (F-statistic):             0.0914 Time:                        02:48:14   Log-Likelihood:                -703.84 No. Observations:                 213   AIC:                             1412. Df Residuals:                     211   BIC:                             1418. Df Model:                           1                                         Covariance Type:            nonrobust      

the low value of F- statistics and P value being greater that 0.025 indicates that we have failed to reject the null hypothesis and we accept the fact that there is no significant difference on the effect of annual income value on the suicide rate

 OLS Regression Results                             ============================================================================== Dep. Variable:        suicideper100th   R-squared:                       0.006 Model:                            OLS   Adj. R-squared:                 -0.004 Method:                 Least Squares   F-statistic:                    0.5930 Date:                Sun, 09 Aug 2020   Prob (F-statistic):              0.554 Time:                        02:48:14   Log-Likelihood:                -704.69 No. Observations:                 213   AIC:                             1415. Df Residuals:                     210   BIC:                             1425. Df Model:                           2                                         Covariance Type:            nonrobust                                         ======================================================================================                         coef    std err          t      P>|t|      [0.025      0.975] -------------------------------------------------------------------------------------- Intercept              7.7779      0.942      8.254      0.000       5.920       9.635 C(Alcoholuse)[T.1]     0.9818      1.204      0.815      0.416      -1.392       3.355 C(Alcoholuse)[T.2]     1.2756      1.190      1.072      0.285      -1.071       3.622

the low value of F- statistics and P value being greater that 0.025 indicates that we have failed to reject the null hypothesis and we accept the fact that there is no significant difference on the effect of alcohol consumption level on the suicide rate.

another analysis have been conducted,which is called the post hoc test, it is used to analyze the difference between the groups of categorical level without increasing the type 1 error in an accumulative manner. we use  the Tuckey honesty test for post hoc comparison. and it agrees with the fact that there is no difference between the alcohol usage levels on the suicide rate .

means for alcohol use by suicide status            suicideper100th Alcoholuse                 0                  7.777891 1                  8.759692 2                  9.053453 standard deviations for alcohol use by suicide            suicideper100th Alcoholuse                 0                  6.086994 1                  5.809631 2                  7.663338 Multiple Comparison of Means - Tukey HSD, FWER=0.05 =================================================== group1 group2 meandiff p-adj   lower  upper  reject ---------------------------------------------------     0      1   0.9818  0.678 -1.8605 3.8241  False     0      2   1.2756 0.5313 -1.5338 4.0849  False     1      2   0.2938    0.9 -2.1713 2.7588  False ---------------------------------------------------

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[inlearncenter]

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[phonemantra-blog]

Eureka teaches more effectively than a human Well, the moment has come when artificial intelligence began to train robots. Nvidia has developed an AI agent called Eureka, which can teach robots complex motor skills. [caption id="attachment_72735" align="aligncenter" width="780"] artificial intelligence[/caption] For example, Eureka taught a robotic hand pen spinning—quickly juggling the handle with its fingers. Of course, a virtual model of the robotic arm was trained, but that doesn't matter. In total, Nvidia's AI agent taught the robots nearly 30 different tasks, including opening cabinet doors, throwing and catching a ball, and so on. Some of these actions may seem very simple, but this is only because we know how to do them automatically and without thinking. https://youtu.be/sDFAWnrCqKc Eureka relies on the GPT-4 language model. Training took place in the Nvidia Isaac Gym physics simulation application. Reinforcement learning has made impressive advances over the past decade, but many challenges still exist, such as reward design, which remains a trial-and-error process. Eureka is the first step towards developing new algorithms that combine generative and reinforcement learning techniques to solve complex problems Artificial intelligence has been created that trains robots. It is important to note that the efficiency of Nvidia's AI agent is very high. The press release said that Eureka's reward programs, which allow robots to learn through trial and error, outperform programs written by experts on more than 80% of tasks. This results in an average increase in bot performance of over 50%. The AI ​​agent uses the GPT-4 language model and generative AI to write code that rewards robots for reinforcement learning. It doesn't require task-specific prompts or predefined reward templates and easily incorporates people's feedback to change rewards to produce results that more closely align with the developer's vision. Using GPU-accelerated simulation in Isaac Gym, Eureka can quickly assess the quality of large batches of reward candidates for more efficient training. Eureka then compiles a summary of key statistics from the training results and instructs the language model to improve the generation of reward functions. Thus, AI improves itself. He has taught all kinds of robots - quadrupeds, bipeds, quadcopters, dexterous robots, manipulator cobots, and others - to perform a variety of tasks