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Developers can perform various experiments with the tools to develop innovative products. The PaLM API will open doors to tremendous opportunities. But before using it, let’s see what the PaLM API is and how developers can access the tool.

What is Google PaLM API?

Google PaLM API is an interface that allows developers to add PaLM (Pathways Language Model) functionalities to generative AI applications. Developers can create innovative products using the PaLM API.

The PaLM API can be used to develop various applications that offer content generation, summarization, translation, chat, and other purposes.

Google has developed a model with limited functionalities, which is small. However, the company will add other models to the API, allowing developers to develop robust applications effortlessly.

Google PaLM API Pricing

Google PaLM is not yet released to the public. Also, the company has not announced the pricing of Google PaLM. The API is still in its testing phase. So, the official price list for Google PaLM API is not available.

The company might release the Google PaLM pricing information after making the API accessible to the general public. Until then, keep checking the official Google PaLM API page for the latest information.

How to Access Google PaLM API?

Google PaLM API is not accessible to the general public. Google is still testing the API within its products. Developers willing to test the Google PaLM API must join the waitlist to access it. Once Google completes the API testing, it will shortlist developers from its waitlist and give them exclusive access to the PaLM API. Till then, you cannot access Google PaLM API.

How to Join Google PaLM API Waitlist?

Since Google allows limited developers to access the Google PaLM API, you cannot get access to the tool without joining the waitlist. You must join the waitlist and wait until Google approves your request.

Enter the details asked on this page and hit the Join with My Google Account button. This will successfully add you to the PaLM API waitlist. Now wait until Google responds to your application.

How to Use PaLM API?

The PaLM API is not officially available to the general public. You cannot use it directly. You need to apply for access to the tool via its waitlist. Once Google approves your application, you can access it as a developer and integrate it into other applications.

For more details on using the PaLM API for text and chat applications or embedding, visit this tutorial.

Alternatively, you can try the PaLM API using the following sample code:

$curl

    -H ‘Content-Type: application/json’

    -d ‘{“prompt”: {“text”: “Write a story about a magic backpack.”}}’

Remember, you will need a Google API key to integrate the API into other applications. You can apply for the API key by joining the waitlist. And once your application is approved, use the Google PaLM API via Python, node, Swift, etc., libraries.

PaLM API Vs Makersuite

PaLM API and Makersuite are both Google products. The PaLM API allows users to integrate PaLM features into other applications and build innovative applications. It allows users to experiment with various features and data while developing the model.

Makersuite is a similar tool but with a low-code approach. Users can use Makersuite to see how a model responds to different inputs. It helps users test the performance and suggests features for improvising the app. The final version is then merged using PaLM API into the software the developer desires to build.

In a nutshell, Makersuite and PaLM API offer a fast and easy way to build innovative AI applications. However, users need to join the waitlist to access both tools.

Google PaLM API Example

Google PaLM API is not yet available to the public. However, some examples of what the API can build are as follows:

Wordcraft – A collaborative AI-based writing partner.

List it – A tool that suggests ideas based on a name or goal.

Quick prompt – An AI game that identifies words using a given prompt.

Travel Planner – An AI-based travel planner that creates itineraries for your trips.

Talking character – An AI-powered customizable talking agent that talks on any topic.

Mood Food – Your AI buddy that gives recipe suggestions based on your mood and ingredients.

I/O Flip – A modern way to play card games.

There are several other uses of the PaLM API. You can check the prompt Gallery for more suggestions.

Google PaLM API Reddit

Google PaLM API Documentation

Google has released the Google PaLM API documentation to help users get started with the API. It has explained the technical specifications and other information on using the API, signing up for the waitlist, and integrating it with other generative AI apps. You can visit the Google PaLM API guide using this link.

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What Is Google Takeout, And How Do You Download Your Google Data Through It?

What data and services does Google Takeout include?

Google

Here are some of the types of data that Google Takeout lets you download:

Account activity and access logs across Google.

Android device configuration data, including device attributes, software versions, account identifiers, and more.

Calendar data.

Bookmarks, history, and other settings from Chrome.

Google Classroom classes, posts, submissions, and more.

Contacts and contact photos.

Files stored in Google Drive.

Photos and videos stored in Google Photos.

Google Fit data such as workouts, sleep, daily steps, and distance.

Data from within Google Pay.

Data from the Google Play Store, like app install, ratings, and orders.

Device, room, home, and history information from the Home app.

Notes and media attachments stored in Google Keep.

Saved locations and settings from Location History; starred places and place reviews from Maps.

Messages and attachments from Gmail.

This is not an exhaustive list by any means. Even if you do not intend to download your data or delete your Google account, you should still check out Google Takeout just to be fascinated by the sheer volume of data that Google has collected from you.

How to use Google Takeout to download your data

Using Google Takeout to download your Google data is a straightforward process. Note that the data can run into several GBs and beyond, depending on various factors. To download the data, follow these steps:

Carefully select the data that you want to download. We recommend checking through the format bubbles, as some of the data presented could include options. For example, you can download Contacts as CSV or vCard, and you could choose one over the other depending on your envisioned use. As mentioned, the size of the data downloaded will increase depending on which services you have chosen.

Aamir Siddiqui / Android Authority

Destination: You can have your data sent to you as a download link via email. Note that you have one week to download your files, after which you must generate a new download link. Alternatively, you can add it directly to an online storage service like Drive, Dropbox, OneDrive, or Box. This will save you the hassle of downloading and re-uploading if your end destination is one of these storage providers.

Frequency: You can export once or have Google Takeout automatically export once every two months for an entire year (so six exports in total). Be mindful of the size of your data export when selecting this.

File type: You can choose your export file extension, either as a ZIP file or as a TGZ file. TGZ bundles tend to be smaller for the same data than ZIP bundles, but ZIP files are better supported on Windows. If you do not know what to choose, choose ZIP.

File size: Since the export is expected to be large, Google will offer to split the export into multiple files, making it easier for you to download. You can choose file splits of 1GB, 2GB, 4GB, 10GB, or 50GB. Note that you would need all the splits together to extract the files. If you do not know what to choose, we recommend 2GB splits.

Depending on the amount of information you have chosen to export, the process can take anywhere from a few minutes to a few days. For most users, you can expect to receive your data within a few hours. However, for users enrolled in Google’s Advanced Protection Program, the archive is scheduled for two days in the future as a security mechanism.

FAQs

Google Takeout is safe to use to download your data but does not provide any further protection to your data. Your data is open to misuse if the exported data falls into the hands of bad actors. Please save your data securely after export.

No, Google Takeout does not delete any data. It provides a mechanism to download your data, but provides no further pathways beyond that. You will have to delete your activity or account independently.

Yes, Google Takeout is free to use.

Google Takeout exports to online storage solutions such as Drive, Dropbox, Box, or OneDrive. Alternatively, you can also receive a direct download link for your data.

Yes. Administrators can view a user’s Google Takeout activity in the audit and investigation page of the Google Admin Console. This lets them see who in the organization has downloaded a copy of their data using Google Takeout.

No. Data that is deleted is handled per Google’s Data Retention Policy. Deleted data or data in the deletion process is not included in your exported archive.

Google Sheets Filter Function: What It Is And How To Use It

The Google Sheets Filter function is a powerful function we can use to filter our data. The Google Sheets Filter function will take your dataset and return (i.e. show you) only the rows of data that meet the criteria you specify (e.g. just rows corresponding to Customer A).

Suppose we want to retrieve all values above a certain threshold? Or values that were greater than average? Or all even, or odd, values?

The Google Sheets Filter function can easily do all of these, and more, with a single formula.

This video is lesson 13 of 30 from my free Google Sheets course: Advanced Formulas 30 Day Challenge.

What is the Filter function?

In this example, we have a range of values in column A and we want to extract specific values from that range, for example the numbers that are greater than average, or only the even numbers.

The filter formula will return only the values that satisfy the conditions we set. It takes two arguments, firstly the full range of values we want to filter and secondly the conditions we’re going to apply. The syntax is:

=FILTER("range of values", "condition 1", ["condition 2", ...])

where Condition 2 onwards are all optional i.e. the Filter function only requires 1 condition to test but can accept more.

How do I use the Filter function in Google Sheets?

For example in the image above, here are the conditions and corresponding formulas:

Conditions

Formula

Filter for

=filter(A3:A21,A3:A21

Filter for even values

=filter(A3:A21,iseven(A3:A21))

Filter for odd values

=filter(A3:A21,isodd(A3:A21))

The results are as follows:

(Note: not all the values are shown in column A.)

Absolutely!

For example, using the basic data above, we could display all the 200-values (i.e. values between 200 and 300) with this formula:

Can I test multiple columns in a Filter function?

Yes, simply add them as additional criteria to test. For example in the following image there are two columns of exam scores. The Filter function used returns all the rows where the score is over 50 in both columns:

The formula is:

Note, using the Filter function with multiple columns like this demonstrates how to use AND logic with the Filter function. Show me all the data where criteria 1 AND criteria 2 (AND criteria 3…) are true.

For OR logic, have a read of this post: Advanced Filter Examples in Google Sheets

Can I reference a criteria cell with the Filter function in Google Sheets?

For example, in this image the Filter function looks to cell E1 for the test criteria, in this case 70, and returns all the values that exceed that score, i.e. everything over 70.

The formula in this example is:

Can I do a filter of a filter?

Yes, you can!

Use the output of your first filter as the range argument of your second filter, like this:

=FILTER( FILTER( range, conditions ), conditions )

Resources

Advanced Filter Examples in Google Sheets

Google documentation for the FILTER function.

Related Articles

Learn how to use the super-powerful Google Sheets Query function to bring the power of SQL to your data, with this comprehensive tutorial and available template.

Learn how to use the super-powerful Google Sheets Query function to bring the power of SQL to your data, with this comprehensive tutorial and available template.

Tensorflow Functional Api: Building A Cnn

This article was published as a part of the Data Science Blogathon

Introduction

In today’s article, I will talk about developing a convolutional neural network employing TensorFlow Functional API. It will dispense the capability of functional API, which allows us to produce hybrid model architecture surpassing the ability of a primary sequential model.

Photo by Rita Morais from Unsplash

About: TensorFlow

TensorFlow is a popular library, something you perpetually hear probably in Deep Learning and Artificial Intelligence society. There are numerous open-source packages and projects for deep learning.

TensorFlow, an open-source artificial intelligence library managing data flow graphs, is the most prevalent deep-learning library. It is used to generate large-scale neural networks with countless layers.

TensorFlow is practiced for deep learning or machine learning predicaments such as Classification, Perception, Perception, Discovery, forecast, and Production.

So, when we interpret a classification problem, we apply a convolutional neural network model. Still, most developers were intimate with modeling sequential models. The layers accompany each other one by one.

The sequential API empowers you to design models layer-by-layer for most significant problems.

The difficulty is restricted in that it does not allow you to produce models that share layers or have added inputs or outputs.

Because of this, we can practice Tensorflows Functional API as Multi-Output Model.

Functional API (tf.Keras)

The functional API in tf.Keras is an alternative way of building more flexible models, including formulating a further complex model.

For example, when implementing an insignificantly more complicated example with machine learning, you may rarely face the state when you demand added models for the same data.

So we would need to produce two outputs. The most manageable option would be to build two separate models based on the corresponding data to make predictions.

This would be gentle, but what if, in a present scenario, we need to have 50 outputs. It could be a discomfort to maintain all those separate models.

Alternatively, it is more fruitful to construct a single model with increased outcomes.

In the open API method, models are determined by forming layers and correlating them straight to each other in sets, then establishing a Model that defines the layers to function as the input and output.

What is different in Sequential API?

Sequential API enables you to generate models layer-by-layer for most top queries. It is regulated because it does not allow you to design models that share layers or have added inputs or outputs.

Let us understand how to create an object of sequential API model below:

model = tf.keras.models.Sequential([ tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(128, activation=’relu’), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(10, activation=’softmax’) ])

In functional API, you can design models that produce a lot more versatility. You can undoubtedly fix models where layers relate to more than just the preceding and succeeding layers.

You can combine layers with several other layers. As a consequence, producing heterogeneous networks such as siamese networks and residual networks becomes feasible.

Let’s begin to develop a CNN model practicing a Functional API

In this post, we utilize the MNIST dataset to build the convolutional neural network for image classification. The MNIST database comprises 60,000 training images and 10,000 testing images secured from American Census Bureau workers and American high school juniors.

# import libraries import numpy as np import tensorflow as tf from tensorflow.keras.layers import Dense, Dropout, Input from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten from tensorflow.keras.models import Model from tensorflow.keras.datasets import mnist # load data (x_train, y_train), (x_test, y_test) = mnist.load_data() # convert sparse label to categorical values num_labels = len(np.unique(y_train)) y_train = to_categorical(y_train) y_test = to_categorical(y_test) # preprocess the input images image_size = x_train.shape[1] x_train = np.reshape(x_train,[-1, image_size, image_size, 1]) x_test = np.reshape(x_test,[-1, image_size, image_size, 1]) x_train = x_train.astype('float32') / 255 x_test = x_test.astype('float32') / 255

In the code above,

I distributed these two groups as train and test and distributed the labels and the inputs.

Independent variables (x_train and x_test) hold greyscale RGB codes of 0 to 255, whereas dependent variables (y_train and y_test) carry labels of 0 to 9, describing which number they genuinely are.

It is a good practice to normalize our data as it is constantly required in deep learning models. We can accomplish this by dividing the RGB codes by 255.

Next, we initialize parameters for the networks.

# parameters for the network input_shape = (image_size, image_size, 1) batch_size = 128 kernel_size = 3 filters = 64 dropout = 0.3

In the code above,

input_shape: variable represents a need to plan and style a standalone Input layer that designates input data. The input layer accepts a shape argument that is a tuple that describes the dimensions of the input data.

batch_size: is a hyperparameter that determines the number of samples to run through before refreshing the internal model parameters.

kernel_size: relates to the dimensions (height x width) of the filter mask. Convolutional neural networks (CNN) are essentially a pile of layers marked by various filters’ operations on the input. Those filters are ordinarily called kernels.

filter: is expressed by a vector of weights among which we convolve the input.

Dropout: is a process where randomly picked neurons are neglected throughout training. This implies that their participation in the activation of downstream neurons is temporally dismissed on the front pass.

Let us define a simplistic Multilayer Perceptron, a convolutional neural network:

# utiliaing functional API to build cnn layers inputs = Input(shape=input_shape) y = Conv2D(filters=filters, kernel_size=kernel_size, activation='relu')(inputs) y = MaxPooling2D()(y) y = Conv2D(filters=filters, kernel_size=kernel_size, activation='relu')(y) y = MaxPooling2D()(y) y = Conv2D(filters=filters, kernel_size=kernel_size, activation='relu')(y) # convert image to vector y = Flatten()(y) # dropout regularization y = Dropout(dropout)(y) outputs = Dense(num_labels, activation='softmax')(y) # model building by supplying inputs/outputs model = Model(inputs=inputs, outputs=outputs) In the code above,

We specify a multilayer Perceptron model toward binary classification.

The model holds an input layer, 3 hidden layers beside 64 neurons, and a product layer with 1 output.

Rectified linear activation functions are applied in all hidden layers, and a softmax activation function is adopted in the product layer for binary classification.

And you can observe the layers in the model are correlated pairwise. This is achieved by stipulating where the input comes from while determining each new layer.

As with every Sequential API, the model is the information we can summarize, fit, evaluate, and apply to execute predictions.

TensorFlow presents a Model class that you can practice to generate a model from your developed layers. It demands that you only define the input and output layers—mapping the structure and model graph of the network architecture.

Lastly, we train the model.

optimizer=’adam’, metrics=[‘accuracy’]) model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=20, batch_size=batch_size) # accuracy evaluation score = model.evaluate(x_test, y_test, batch_size=batch_size, verbose=0) print(“nTest accuracy: %.1f%%” % (100.0 * score[1]))

Now we have successfully developed a convolutional neural network to distinguish handwritten digits with Tensorflow’s Functional API. We have obtained an accuracy of above 99%, and we can save the model & design a digit-classifier web application.

References:

The media shown in this article on Sign Language Recognition are not owned by Analytics Vidhya and are used at the Author’s discretion.

Related

What Is Generative Ai?

Generative AI is the use of artificial intelligence (AI) systems to generate original media such as text, images, video, or audio in response to prompts from users. Popular generative AI applications include ChatGPT, Bard, DALL-E, and Midjourney.

Most generative AI is powered by deep learning technologies such as large language models (LLMs). These are models trained on a vast quantity of data (e.g., text) to recognize patterns so that they can produce appropriate responses to the user’s prompts.

This technology has seen rapid growth in sophistication and popularity in recent years, especially since the release of ChatGPT in November 2023. The ability to generate content on demand has major implications in a wide variety of contexts, such as academia and creative industries.

How does generative AI work?

Generative AI is a broad concept that can theoretically be approached using a variety of different technologies. In recent years, though, the focus has been on the use of neural networks, computer systems that are designed to imitate the structures of brains.

Highly complex neural networks are the basis for large language models (LLMs), which are trained to recognize patterns in a huge quantity of text (billions or trillions of words) and then reproduce them in response to prompts (text typed in by the user).

An LLM generates each word of its response by looking at all the text that came before it and predicting a word that is relatively likely to come next based on patterns it recognizes from its training data. You can think of it as a supercharged version of predictive text. The fact that it generally works so well seems to be a product of the enormous amount of data it was trained on.

LLMs, especially a specific type of LLM called a generative pre-trained transformer (GPT), are used in most current generative AI applications—including many that generate something other than text (e.g., image generators like DALL-E). This means that things like images, music, and code can be generated based only on a text description of what the user wants.

Types of generative AI

Generative AI has a variety of different use cases and powers several popular applications. The table below indicates the main types of generative AI application and provides examples of each.

Strengths and limitations of generative AI

Generative AI is a powerful and rapidly developing field of technology, but it’s still a work in progress. It’s important to understand what it excels at and what it tends to struggle with so far.

Strengths

Generative AI technology is often flexible and can generalize to a variety of tasks rather than specializing in just one. This opens up opportunities to explore its use in a wide range of contexts.

This technology can make any business processes that involve generating text or other content (e.g., writing emails, planning projects, creating images) dramatically more efficient, allowing small teams to accomplish more and bigger teams to focus on more ambitious projects.

Generative AI tools allow non-experts to approach tasks they would normally be unable to handle. This allows people to explore areas of creativity and work that were previously inaccessible to them.

Limitations

Generative AI models often hallucinate—for example, a chatbot’s answers might be factually incorrect, or an image generator’s outputs might contain incongruous details like too many fingers on a person’s hand. Outputs should always be checked for accuracy and quality.

These tools are trained on datasets that may be biased in various ways (e.g., sexism), and the tools can therefore reproduce those biases. For example, an image generator asked to provide an image of a CEO may be more likely to show a man than a woman.

Although they’re trained on large datasets and draw on all that data for their responses, generative AI tools generally can’t tell you what sources they’re using in a specific response. This means it can be difficult to trace the sources of, for example, factual claims or visual elements.

Implications of generative AI

The rise of generative AI raises a lot of questions about the effects—positive or negative—that different applications of this technology could have on a societal level. Commonly discussed issues include:

Jobs and automation: Many people are concerned about the effects of generative AI on various creative jobs. For example, will it be harder for illustrators to find work when they have to compete with image generators? Others claim that these tools will force various industries to adapt but also create new roles as existing tasks are automated.

Effects on academia: Many academics are concerned about ChatGPT cheating among their students and about the lack of clear guidelines on how to approach these tools. University policies on AI writing are still developing.

Plagiarism and copyright concerns: Some argue that generative AI’s use of sources from its training data should be treated as plagiarism or copyright infringement. For example, some artists have attempted legal action against AI companies, arguing that image generators use elements of their work and stylistic approach without acknowledgement or compensation.

Fake news and scams: Generative AI tools can be used to deliberately spread misinformation (e.g., deepfake videos) or enable scams (e.g., imitating someone’s voice to steal their identity). They can also spread misinformation by accident if people assume, for example, that everything ChatGPT claims is factually correct without checking it against a credible source.

Future developments: There is a lot of uncertainty about how AI is likely to develop in the future. Some argue that the rapid developments in generative AI are a major step towards artificial general intelligence (AGI), while others suspect that we’re reaching the limits of what can be done with current approaches to AI and that future innovations will use very different techniques.

Other interesting articles

If you want to know more about ChatGPT, AI tools, fallacies, and research bias, make sure to check out some of our other articles with explanations and examples.

Frequently asked questions about generative AI Cite this Scribbr article

Caulfield, J. Retrieved July 19, 2023,

Cite this article

What Is A Customer Data Platform And What Is Use It?

A Customer Data Platform, also known as AI HTML3_, is a piece of HTML3_ programming HTML3_ that combines information from different apparatuses.

What is a Customer Data Platform (CDP)?

A Customer Data Platform (CDP), a piece of AI programming, is an AI program that blends data from different apparatuses to create a single concentrated client data set. It contains information on all touchpoints and associations with your administration or item. The CDP data protection database can be divided in a variety of ways. This allows for more targeted showcasing efforts and data protection.

Understanding what CDP programming does, the most effective way to make sense of this is as a visual demonstration. Say an organization is attempting to get a superior comprehension of their clients. The CDP will be used to collect information from social media platforms such as Facebook and email. The CDP will collect the data, then combine it into a client profile that can be used for other purposes, such as the Facebook promotions stage.

This cycle allows the organization to use division to better understand its audience and to make more targeted showcasing efforts. The organization could undoubtedly make a publicizing crowd in light of every individual who has visited a particular page on their site and furthermore the organization’s live talk include. Or on the other hand, they could rapidly fragment and view information on location guests who’ve deserted their trucks.

This is one way Drift can customize its showcasing efforts. Segment’s Personas are used to help with three tasks.

Personality goal – To unify client history across all gadgets and channels, into one client view for each client.

Quality and crowd-building – Synthesizes data into crowds and attributes for each client, including clients who have expressed expectations. This coordinates with generally speaking record movements.

Actuation- This pushes their client to different instruments in their stack in order to coordinate custom, continuous outbound information.

How to Use a Customer Data Platform?

1. Online to Offline Connection

Combine offline and online activities to build a customer profile. When customers enter a brick-and-mortar store, you can identify them from their online activities.

2. Customer Segmentation & Personalization

3. Predictive Customer Scoring

Enhance your customer profiles by using predictive data (probability to purchase, churn and visit, email open).

4. Smart Behavioral Retargeting & Looking Alike Advertising

Also read:

Top 10 Best Artificial Intelligence Software

5. Recommendations for Product

6. Conversion Rate Optimization and A/B Testing

You can quickly transform your pages’ appearance. Our smart website overlays (popups) or cart abandonment emails can help you increase your ROI. You can create different designs and see which one performs best with automation.

Also read:

Top 10 Trending Technologies You should know about it for Future Days

7. Omni-Channel Automation

8. Email Delivery Enhancement

Increase email opening rates. An AI-powered algorithm allows you to determine the best time to distribute each user’s email based on their email opening patterns and reach them at that optimal hour.

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