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[impact-newswire]

Scale Generative AI Platform leverages your enterprise data to customize powerful base generative models to safely unlock the value of AI.

[mindschild]

https://www.washingtonpost.com/world/2023/08/28/scale-ai-remotasks-philippines-artificial-intelligence/

In the Philippines, a significant number of workers engage in "crowdwork," annotating data for AI training in dingy internet cafes, jam-packed offices, or at home. These workers, often exploited and underpaid, contribute to the backbone of AI by providing accurate and precise data for machine learning algorithms. Platforms like Remotasks, owned by Scale AI, employ these workers, but allegations suggest that Scale AI has paid workers extremely low rates, delayed payments, and provided limited channels for recourse. Many workers earn well below minimum wage, and instances of late or canceled payments are common. These practices raise ethical concerns about labor exploitation within the AI industry, with many workers earning far less than they should. Efforts to regulate such platforms in the Philippines are challenging, while the global data annotation industry is projected to grow significantly.

[happynorasullivan]

Scale 13 Reviews - Looking to Have AI Make Sales Calls?

Technology is moving quickly. Here's how to have AI make your calls…

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How can you have artificial intelligence make your sales calls?

AI can help you develop targeted sales plays by analyzing past sales data and identifying patterns of success. Many CRM systems have built-in automated sequences that automatically send emails and alert you to tasks. You can also use ChatGPT or other AI software to build each step of your sales play.

What is AI Cold Calling?

Artificial intelligence cold calling or AI-cold calling leverages artificial intelligence to provide sales teams insights into cold calls to launch previously scheduled meetings and deliver top-of-the-funnel results. It can be a massive advantage for sales teams that especially rely on manual methods to make cold calls. 

According to McKinsey, sales professionals that have adopted AI have increased leads and appointments by about 50%. But you have to be smart about using it to your best advantage.

More Calls Made

There are predictive or power dialers that help sales reps make way more outbound calls at scale, and then there are automations that will pull in activity or call data without reps having to lift a finger.

How does AI-generated voice work?

AI voice generators use deep learning algorithms and neural networks to create lifelike speech that sounds natural. They are an increasingly popular tool for generating voiceovers in various applications.

With AI, sales reps can also leverage post-call reporting that does not require much manual intervention. 

Many marketers have gotten tired of dialing answering machines and paying inefficient appointment setters to "book a call" and deal with dozens of no-shows!

The Bottom Line:

Although some marketers may lead you to believe that AI can actually have a conversation with prospects and close sales calls this is not the case. If you are using the telephone to make sales you, or someone you pay, must still make the sales call and do the closing.

But you can use AI and automation tools to automate tasks such as:

- finding prospects, 

- verifying contact information, 

- scheduling calls, 

- sending follow-up emails, 

- and updating your CRM. 

Even if you decide to use robots you are still dealing with humans.

And most humans HATE sales calls!

Most Americans don’t answer cellphone calls from unknown numbers.

“Americans just aren’t picking up the phone much anymore. Eight-in-ten Americans say they don’t generally answer their cellphone when an unknown number calls, according to newly released findings from a Pew Research Center web survey of U.S. adults...”

If you are trying to earn a full-time income online consider joining our students working on six-figure incomes part-time. This means you can still keep your job while enjoying your new income stream.

Discover how our students are earning income online without:

- phone calls

- paying for ads

- webinars

- and using AI to make calls

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[madscientist008]

Meet Alexander Wang, 25, The World's Youngest Self-Made Billionaire

You may have heard of Scale AI, the San Francisco-based company that helps hundreds of businesses unlock the potential of their data with artificial intelligence. But do you know the story behind its founder and CEO, Alexandr Wang? Alexandr Wang is not your typical tech billionaire. He grew up in New Mexico, where his parents were physicists working on weapons projects for the military. He was a math prodigy who competed in national contests and landed a full-time coding job at Quora when he was 17. He enrolled at MIT to study machine learning, but dropped out after his freshman year to start Scale AI with his cofounder, Lucy Guo. Scale AI provides a platform that connects companies with a network of human annotators who label and organize massive amounts of data, such as images, text, and audio. This data is then used to train and improve machine learning models for various applications, such as self-driving cars, satellite imagery analysis, and e-commerce. Scale AI has more than 300 clients, including General Motors, Flexport, and the US Air Force and Army. In 2021, Scale AI raised $325 million in a funding round that valued the company at $7.3 billion. Wang's estimated 15% stake in the company made him the world's youngest self-made billionaire, with a net worth of $1 billion, according to Forbes. He was only 25 years old at the time. Wang is not just a visionary entrepreneur, but also a generous philanthropist. He has donated millions of dollars to causes such as Covid-19 relief, education, and diversity in tech. He also supports young innovators and aspiring entrepreneurs through mentorship and investment. Alexandr Wang is an inspiration to anyone who dreams of making a positive impact on the world with technology. He proves that age is no barrier to success, and that passion, curiosity, and hard work can lead to amazing achievements.

[industrydesignservices]

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[newscheckz]

What is semi-supervised machine learning?

New Post has been published on https://newscheckz.com/what-is-semi-supervised-machine-learning/

What is semi-supervised machine learning?

Machine learning has proven to be very efficient at classifying images and other unstructured data, a task that is very difficult to handle with classic rule-based software.

But before machine learning models can perform classification tasks, they need to be trained on a lot of annotated examples.

Data annotation is a slow and manual process that requires humans to review training examples one by one and giving them their right labels.

In fact, data annotation is such a vital part of machine learning that the growing popularity of the technology has given rise to a huge market for labeled data.

From Amazon’s Mechanical Turk to startups such as LabelBox, ScaleAI, and Samasource, there are dozens of platforms and companies whose job is to annotate data to train machine learning systems.

Fortunately, for some classification tasks, you don’t need to label all your training examples.

Instead, you can use semi-supervised learning, a machine learning technique that can automate the data-labeling process with a bit of help.

Supervised vs unsupervised vs semi-supervised machine learning

You only need labeled examples for supervised machine learning tasks, where you must specify the ground truth for your AI model during training. Examples of supervised learning tasks include image classification, facial recognition, sales forecasting, customer churn prediction, and spam detection.

Unsupervised learning, on the other hand, deals with situations where you don’t know the ground truth and want to use machine learning models to find relevant patterns. Examples of unsupervised learning include customer segmentation, anomaly detection in network traffic, and content recommendation.

Semi-supervised learning stands somewhere between the two. It solves classification problems, which means you’ll ultimately need a supervised learning algorithm for the task.

But at the same time, you want to train your model without labeling every single training example, for which you’ll get help from unsupervised machine learning techniques.

Semi-supervised learning with clustering and classification algorithms

One way to do semi-supervised learning is to combine clustering and classification algorithms.

Clustering algorithms are unsupervised machine learning techniques that group data together based on their similarities.

The clustering model will help us find the most relevant samples in our data set. We can then label those and use them to train our supervised machine learning model for the classification task.

Say we want to train a machine learning model to classify handwritten digits, but all we have is a large data set of unlabeled images of digits.

Annotating every example is out of the question and we want to use semi-supervised learning to create your AI model.

First, we use k-means clustering to group our samples. K-means is a fast and efficient unsupervised learning algorithm, which means it doesn’t require any labels.

K-means calculates the similarity between our samples by measuring the distance between their features.

In the case of our handwritten digits, every pixel will be considered a feature, so a 20×20-pixel image will be composed of 400 features.

  K-means clustering is a machine learning algorithm that arranges unlabeled data points around a specific number of clusters.

  When training the k-means model, you must specify how many clusters you want to divide your data into. Naturally, since we’re dealing with digits, our first impulse might be to choose ten clusters for our model.

But bear in mind that some digits can be drawn in different ways. For instance, here are different ways you can draw the digits 4, 7, and 2.

You can also think of various ways to draw 1, 3, and 9.

Therefore, in general, the number of clusters you choose for the k-means machine learning model should be greater than the number of classes.

In our case, we’ll choose 50 clusters, which should be enough to cover different ways digits are drawn.

After training the k-means model, our data will be divided into 50 clusters. Each cluster in a k-means model has a centroid, a set of values that represent the average of all features in that cluster.

We choose the most representative image in each cluster, which happens to be the one closest to the centroid. This leaves us with 50 images of handwritten digits.

Now, we can label these 50 images and use them to train our second machine learning model, the classifier, which can be a logistic regression model, an artificial neural network, a support vector machine, a decision tree, or any other kind of supervised learning engine.

Training a machine learning model on 50 examples instead of thousands of images might sound like a terrible idea.

But since the k-means model chose the 50 images that were most representative of the distributions of our training data set, the result of the machine learning model will be remarkable.

In fact, the above example, which was adapted from the excellent book Hands-on Machine Learning with Scikit-Learn, Keras, and Tensorflow, shows that training a regression model on only 50 samples selected by the clustering algorithm results in a 92-percent accuracy (you can find the implementation in Python in this Jupyter Notebook).

In contrast, training the model on 50 randomly selected samples results in 80-85-percent accuracy.

But we can still get more out of our semi-supervised learning system. After we label the representative samples of each cluster, we can propagate the same label to other samples in the same cluster.

Using this method, we can annotate thousands of training examples with a few lines of code. This will further improve the performance of our machine learning model.

Other semi-supervised machine learning techniques

There are other ways to do semi-supervised learning, including semi-supervised support vector machines (S3VM), a technique introduced at the 1998 NIPS conference.

S3VM is a complicated technique and beyond the scope of this article. But the general idea is simple and not very different from what we just saw: You have a training data set composed of labeled and unlabeled samples.

S3VM uses the information from the labeled data set to calculate the class of the unlabeled data, and then uses this new information to further refine the training data set.

  The semi-supervised support vector machine (S3VM) uses labeled data to approximate and adjust the classes of unlabeled data.

  If you’re are interested in semi-supervised support vector machines, see the original paper and read Chapter 7 of Machine Learning Algorithms, which explores different variations of support vector machines (an implementation of S3VM in Python can be found here).

An alternative approach is to train a machine learning model on the labeled portion of your data set, then using the same model to generate labels for the unlabeled portion of your data set. You can then use the complete data set to train an new model.

The limits of semi-supervised machine learning

Semi-supervised learning is not applicable to all supervised learning tasks. As in the case of the handwritten digits, your classes should be able to be separated through clustering techniques.

Alternatively, as in S3VM, you must have enough labeled examples, and those examples must cover a fair represent the data generation process of the problem space.

But when the problem is complicated and your labeled data are not representative of the entire distribution, semi-supervised learning will not help.

For instance, if you want to classify color images of objects that look different from various angles, then semi-supervised learning might help much unless you have a good deal of labeled data (but if you already have a large volume of labeled data, then why use semi-supervised learning?).

Unfortunately, many real-world applications fall in the latter category, which is why data labeling jobs won’t go away any time soon.

But semi-supervised learning still has plenty of uses in areas such as simple image classification and document classification tasks where automating the data-labeling process is possible.

Semi-supervised learning is a brilliant technique that can come handy if you know when to use it.

This article was originally published by Ben Dickson on TechTalks, a publication that examines trends in technology, how they affect the way we live and do business, and the problems they solve. But we also discuss the evil side of technology, the darker implications of new tech and what we need to look out for. You can read the original article here. 

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