Introduction

As the demand for smarter, more automated systems grows across industries, machine learning (ML) and deep learning (DL) have become the backbone of innovation. From self-driving cars to personalized recommendations, AI applications are transforming the way we live and work. One of the key frameworks driving this revolution is TensorFlow.

TensorFlow is an open-source library developed by Google that enables developers to build, train, and deploy machine learning and deep learning models. It is known for its flexibility, scalability, and efficiency in handling large datasets and complex algorithms. Whether you’re a researcher, data scientist, or developer, TensorFlow provides a powerful toolkit to solve a wide variety of problems using AI.

In this blog, we will explore what TensorFlow is, its top 10 use cases, the features that make it popular, how TensorFlow works and its architecture, the process to install TensorFlow, and provide basic tutorials to help you get started with TensorFlow.

What is TensorFlow?

TensorFlow is an open-source software library primarily used for machine learning (ML) and deep learning (DL) applications. Developed by the Google Brain team, it provides a robust platform for building and training machine learning models, performing numerical computation, and conducting research in AI. TensorFlow supports a wide range of tasks, from simple linear regression to complex neural network models used in image and speech recognition.

TensorFlow offers a high-level interface for ease of use and is highly optimized for both CPU and GPU processing, making it ideal for large-scale machine learning applications. It is widely used by researchers, data scientists, and engineers to develop state-of-the-art AI models and applications.

Top 10 Use Cases of TensorFlow

TensorFlow’s versatility allows it to be applied in various domains. Below are the top 10 use cases where TensorFlow excels:

1. Image Classification and Computer Vision

One of the most popular use cases for TensorFlow is image classification. Using deep learning models, TensorFlow can be trained to recognize objects within images. Applications include facial recognition, object detection, and medical image analysis, where TensorFlow models can help identify diseases from scans like X-rays or MRIs.

2. Natural Language Processing (NLP)

TensorFlow is widely used in natural language processing (NLP) for tasks such as sentiment analysis, text classification, language translation, and speech recognition. With the help of recurrent neural networks (RNNs) and transformers, TensorFlow enables machines to understand and process human language more effectively.

3. Recommendation Systems

Recommendation systems, such as the ones used by Netflix, Amazon, and YouTube, rely heavily on machine learning algorithms. TensorFlow is often used to build and train recommendation models that analyze user preferences and behaviors to suggest relevant content, products, or services.

4. Speech Recognition and Synthesis

TensorFlow plays a key role in speech recognition systems, such as voice assistants like Google Assistant or Alexa. It is used to train models that convert spoken language into text (speech-to-text) and vice versa (text-to-speech). Additionally, TensorFlow can be used to build systems that recognize specific voice commands or transcribe audio recordings.

5. Time Series Prediction and Forecasting

In industries like finance, energy, and healthcare, TensorFlow is used to predict future values based on historical data. Time series forecasting models built with TensorFlow can help forecast stock prices, energy consumption, demand for products, and patient health outcomes.

6. Autonomous Vehicles

Self-driving cars rely on deep learning and computer vision to navigate and make decisions. TensorFlow is used in training models that help autonomous vehicles interpret sensor data (like cameras, LiDAR, and radar), identify obstacles, and make real-time decisions on the road.

7. Anomaly Detection and Fraud Detection

TensorFlow is widely used in anomaly detection applications, where it identifies unusual patterns in data. For example, in fraud detection, TensorFlow models can analyze transaction data in real time and flag suspicious activities, such as unauthorized credit card usage or identity theft.

8. Generative Models (GANs)

TensorFlow is used to create Generative Adversarial Networks (GANs), which are a class of machine learning models that can generate new, synthetic data based on patterns learned from existing datasets. GANs are widely used in image generation, video creation, art generation, and more.

9. Healthcare and Medical Research

In healthcare, TensorFlow is applied to analyze medical images, predict disease outbreaks, and help researchers find new drug treatments. TensorFlow can be used for disease prediction (e.g., cancer detection), genomics, and personalized medicine, enabling better outcomes for patients.

10. Robotics and AI in Manufacturing

TensorFlow is used in the development of intelligent robots that can perform tasks like object manipulation, picking, and assembly in manufacturing environments. These robots rely on deep learning models to interpret sensory data and make autonomous decisions to carry out complex tasks.

What Are the Features of TensorFlow?

TensorFlow offers a wide range of features that make it a popular choice for machine learning practitioners. Some key features include:

• Open-Source: TensorFlow is open-source, which means it is free to use and can be customized to meet specific needs.
• Scalability: TensorFlow is designed for scalability, enabling users to run models on everything from personal computers to distributed clusters and cloud environments.
• Cross-Platform Support: TensorFlow supports various platforms, including desktop, mobile (Android/iOS), and embedded systems.
• GPU/TPU Acceleration: TensorFlow supports GPU and TPU acceleration, which enables faster training of deep learning models.
• TensorFlow Serving: For deploying machine learning models in production, TensorFlow provides tools like TensorFlow Serving for serving models at scale.
• TensorFlow Lite: A lightweight version of TensorFlow designed for mobile and embedded devices, allowing AI models to be deployed on smartphones, IoT devices, and edge computing platforms.
• Pre-trained Models: TensorFlow offers many pre-trained models, which can be fine-tuned for specific use cases, reducing the time required to build and train models from scratch.
• Eager Execution: TensorFlow supports eager execution for immediate feedback and debugging, which allows you to run operations immediately as they are called.
• Keras Integration: Keras, a high-level neural network API, is integrated into TensorFlow, providing an easy-to-use interface for building deep learning models.
• Extensive Ecosystem: TensorFlow has a rich ecosystem of tools and libraries, such as TensorFlow Extended (TFX), TensorFlow Hub, and TensorFlow.js, to help with model deployment, feature engineering, and more.

How TensorFlow Works and Architecture

TensorFlow’s architecture is designed for flexibility and scalability. The framework is based on the concept of dataflow graphs, where computations are represented as a graph of nodes, with each node performing a mathematical operation. These graphs are made up of tensors, which are multi-dimensional arrays that flow through the graph during computation.

Here’s how TensorFlow works:

1. Graph Construction: You first define a graph that specifies how data will flow through the operations.
2. Session Execution: Once the graph is defined, you can execute it within a session. The data is passed through the graph, and the operations are executed.
3. Tensors: Data within the graph is represented as tensors. Tensors are the fundamental data structure in TensorFlow and are used to represent data arrays of any shape and dimension.
4. Operations: Operations are mathematical functions (e.g., addition, multiplication) that are applied to tensors to process and transform data.

TensorFlow can run these computations on a variety of devices, including CPUs, GPUs, and TPUs (Tensor Processing Units). This makes TensorFlow highly scalable, allowing it to be used for both small-scale projects and large, distributed systems.

How to Install TensorFlow?

Installing TensorFlow is easy and can be done in a few simple steps. Here’s how to install TensorFlow on your system:

1. Install TensorFlow via pip

The easiest way to install TensorFlow is using pip, the Python package manager. Run the following command in your terminal:

pip install tensorflow

If you’re using Python 3, use:

pip3 install tensorflow

2. Install TensorFlow with GPU Support

To take advantage of GPU acceleration, you can install the GPU version of TensorFlow by running:

pip install tensorflow-gpu

Ensure that you have the required GPU drivers and CUDA toolkit installed for GPU support.

3. Verify Installation

Once installed, verify that TensorFlow is installed correctly by running the following code in Python:

import tensorflow as tf
print(tf.__version__)

If TensorFlow is installed correctly, it will display the version number.

Basic Tutorials of TensorFlow: Getting Started

1. Building a Simple Neural Network

A common starting point is building a simple neural network for classification tasks. Here’s a basic example of building a neural network to classify the MNIST dataset (handwritten digits):

import tensorflow as tf
from tensorflow.keras import layers, models

# Load dataset
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

# Preprocess data
x_train, x_test = x_train / 255.0, x_test / 255.0

# Build model
model = models.Sequential([
    layers.Flatten(input_shape=(28, 28)),
    layers.Dense(128, activation='relu'),
    layers.Dropout(0.2),
    layers.Dense(10, activation='softmax')
])

# Compile model
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# Train model
model.fit(x_train, y_train, epochs=5)

# Evaluate model
model.evaluate(x_test, y_test)

2. Working with TensorFlow Datasets

TensorFlow provides a convenient way to work with datasets, including built-in datasets like MNIST. You can load and preprocess datasets using tf.data API, which provides an efficient way to input data into your model.

3. Saving and Loading Models

Once a model is trained, you can save it and reload it for future use:

# Save model
model.save('my_model.h5')

# Load model
loaded_model = tf.keras.models.load_model('my_model.h5')

The Power of TensorFlow for Machine Learning and AI

TensorFlow is a powerful, flexible, and scalable framework that enables businesses, researchers, and developers to build cutting-edge machine learning and deep learning models. Whether you’re working on image recognition, natural language processing, or time series forecasting, TensorFlow provides the tools and infrastructure needed to train, test, and deploy complex AI systems.

With its comprehensive features, rich ecosystem, and strong community support, TensorFlow continues to be a top choice for machine learning practitioners and organizations looking to leverage AI for innovative solutions.

The post What is TensorFlow and Use Cases of TensorFlow? appeared first on Artificial Intelligence.

Pharmaceutical professionals believe artificial intelligence (AI)will be the most disruptive technology in the industry in 2021. As AI and machine learning (ML) become crucial tools for keeping pace in the industry, clinical development is an area that can substantially benefit, delivering significant time and cost efficiencies while providing better, faster insights to inform decision making. However, for patients, these tools provide improved safety practices that lead to better, safer, drugs. Here is how AI/ML can be used to support pharma companies in delivering safer drugs to market.

Overcoming Barriers to Using AI in Clinical Research

Today, AI and ML can be used to support clinical research in numerous ways; including the identification of molecules that hold potential for clinical treatments, finding patient populations that meet specific criteria for inclusion or exclusion, as well as analyzing scans, claims reports, and other healthcare data to identify trends in clinical research and treatments that lead to safer and faster decisions.

However, to take full advantage of the benefits of AI/ML technology, organizations performing clinical trials must first gain access to the tools, expertise, and industry-specific datasets enabling them to build algorithms to fit their specific needs. Healthcare data, unlike purely numerical data pulled from monitoring systems and tools such as IoT or SaaS platforms, is typically unstructured due to the way the data is collected (through doctor visits, and unstructured web sources) and must meet strict security protocols to ensure patient privacy.

To truly leverage AI and ML for clinical research, data must be collected, studied, combined, and protected to make effective healthcare decisions. When clinical researchers collaborate with partners that have both technical and pharmaceutical expertise, they ensure that data is being structured and analyzed in a way that simultaneously reduces risks and improves the quality of clinical research.

The Benefits of AI for Clinical Research

When it comes to research study design, site identification and patient recruitment, and clinical monitoring, AI and ML hold great potential to make clinical trials faster, more efficient, and most importantly: safer.

Study design sets the stage for a clinical research initiative. The cost, efficiency, and potential success of clinical trials rest squarely on the shoulders of the study’s design and plans. AI and ML tools, along with natural language processing (NLP), can analyze large sets of healthcare data to assess and identify primary and secondary endpoints in clinical research design. This ensures that protocols for regulators, payers, and patients are well defined before clinical trials commence. Defining parameters such as these optimize study design by helping to identify ideal research sites and enrollment models. Ultimately, better study design leads to more predictable results, reduced cycle time for protocol development, and a generally more efficient study.

Identifying trials sites and recruiting patients for clinical research is a tougher task than it seems to be at face value. Clinical researchers must identify the area that will provide enough access to patients who meet inclusion and exclusion criteria. As studies become more focused on rarer conditions or specific populations, recruiting participants for clinical trials becomes more difficult, which increases the cost, timeline, and risk of failure for the clinical study if enough patients cannot be recruited for the research. AI and ML tools can support site identification for clinical research by mapping patient populations and proactively targeting sites with the most potential patients that meet inclusion criteria. This enables fewer research sites to meet recruitment requirements and reduce the overall cost of patient recruitment.

Clinical monitoring is a tedious manual process of analyzing site risks of clinical research and determining specific actions to take towards mitigating those risks. Risks in clinical research include recruitment or performance issues, as well as risks to patient safety. AI and ML automate the assessment of risks in the clinical research environment, and provide suggestions based on predictive analytics to better monitor for and prevent risks. Automating this assessment removes the risk of manual error, and decreases the time spent on analyzing clinical research data.

Strategies for Using AI for Clinical Research

During clinical trials, there’s a limited patient population to pull from, as research subjects must meet pre-set parameters for inclusion in the study. On the other hand, as opposed to post-market research, clinical researchers are blessed with vast amounts of information surrounding their patients including what drugs they are taking, their health history, and their current environment.

In addition, because the clinical researcher is working closely with the patient and is well-educated on the drug or product being researched, the researcher is very familiar with all potential variables involved in the clinical trial. To put it simply, clinical trials have a lot of information to analyze, but few patients with whom to conduct the research. Because of this disproportionate ratio of information over patients, every case in a clinical research setting is extremely important to the future of the drug being researched.

The massive amount of patient and drug information available to clinical researchers necessitates the use of NLP tools to analyze and process documents and patient records.NLP can search documents and records for specific terms, phrases, and words that might indicate a problem or risk in the clinical trial. This eliminates the need for manual analysis of clinical trial data – reducing, and in some cases eliminating, the risk of human error while also increasing patient safety. This is especially useful in lengthy clinical trials, for which researchers will need to analyze patient histories and drug results over an extended period of time. Many clinical trials have long document trails and questionnaires that can add up to hundreds of pages of patient data that researchers must analyze.

In a clinical trial, researchers are ultimately trying to determine whether the benefits of a specific treatment outweigh the risks. AI can be especially helpful in clinical trials of high-risk drugs. If a researcher knows that a drug cures or alleviates an illness or condition, but also know that the potential side effects of that drug can have a significant negative impact on the patient, they’ll want to know how to determine if a patient is likely to present those negative side effects. NLP can be used to produce word clouds of potential signals of the negative side effects of a drug that patients would experience.

The only way to do this type of analysis manually is to identify those words using human researchers, then analyze the patient reports to find those words, and group those reports into risk profiles. NLP can automate that entire process and provide insights on risk indicators in patients much more efficiently and safely than human researchers ever could.

Integrating AI & ML with Clinical Research Creates Competitive Results

AI and ML technologies, especially NLP, hold huge promise to support and optimize clinical research. However, that assurance can only be achieved by organizations that have the necessary tools, expertise, and partners to leverage the full benefits of AI and ML. AI and ML solutions support the optimization of clinical research by more efficiently analyzing research data for risks and allowing faster trial planning and research. Those who fail to engage AI and ML for clinical research may find that their competitors are doing so, and as a result, are going to market with new drugs and products faster with higher profits due to decreased research time and safer practices.

Author

Updesh Dosanjh, Practice Leader, Pharmacovigilance Technology Solutions, IQVIA

As Practice Leader for the Technology Solutions business unit of IQVIA, Updesh Dosanjh is responsible for developing the overarching strategy regarding Artificial Intelligence and Machine Learning as it relates to safety and pharmacovigilance. He is focused on the adoption of these innovative technologies and processes that will help optimize pharmacovigilance activities for better, faster results.  Dosanjh has over 25 years of knowledge and experience in the management, development, implementation, and operation of processes and systems within the life sciences and other industries.  Most recently, Dosanjh was with Foresight and joined IQVIA as a result of an acquisition. Over the course of his career, Dosanjh also worked with WCI, Logistics Consulting Partners, Amersys Systems Limited, and FJ Systems. Dosanjh holds a Bachelor’s degree in Materials Science from Manchester University and a Master’s degree in Advanced Manufacturing Systems and Technology from Liverpool University.

The post SEIZING THE OPPORTUNITY TO LEVERAGE AI & ML FOR CLINICAL RESEARCH appeared first on Artificial Intelligence.

AI has now made it onto CEOs’ agendas. While the topic certainly isn’t new, CEOs have learned that the idea of AI is far simpler than its effective application. To get there, companies need to start with their business objectives and then use AI in ways that advance those objectives rather than just implementing AI for AI’s sake and hoping it can add value later.

Meanwhile, CEO attitudes about AI and machine learning or ML (a subset of AI techniques) have been changing as it relates to digital disruption. In the beginning, it was about understanding what digital disrupters do and how they do it. Now, they’re beginning to realize that they need to create value on their own terms. That’s not to say that they won’t take advantage of some of the accelerators the digital giants have made freely available. However, a me-too only strategy is ultimately a risky proposition.

“The top-most priority for the CEOs of leading companies is reinventing the future of their companies, powered with AI, powered with data,” said Arnab Chakraborty, global managing director, applied intelligence North America Lead at multinational consulting firm Accenture. “It’s about unlocking the value with AI by looking at how they optimize their existing business whether it’s in sales and marketing, supply chain, finance, HR, all those functions.”

Not surprisingly, AI is also on CIOs’ agendas. In fact, global professional services company Genpact recently published a report with MIT Sloan CIO Symposium in which 48% of the 500 CIOs surveyed said that AI is their No. 1 investment priority.

“CIOs are saying, we’ve got to invest in it. The question is, why now?” said Sanjay Srivastava, chief digital officer at global professional services firm Genpact. “Three things have happened: the technology has gotten very good; it has become very affordable; and the need has gone up.”

The post 10 Ways AI and ML Are Evolving appeared first on Artificial Intelligence.

In times like now, we have an increasing amount of modern technological advancement all over the world. Some say it’s a blessing and some argue calling it a curse. Though, the idea of development gets a huge boost when there is an efficient approach to the technology involved. Many sectors across the world are enjoying their piece of success through technology and computer science enhancements. The new age of technological advancement is said to be AI (Artificial Intelligence) and ML (Machine Learning).  

The last few years have been revolutionary for technology and an introduction to AI and ML was just the head start mankind needed. ML and AI have been supporting many sectors providing the comfort of accuracy and efficiency. But the educational sector has been under a solid rejuvenation mode over the last few years. Bringing AI and ML into education is proving to be revolutionary as well as profitable. In the education sector, teaching, and learning have become more attractive and advanced after the introduction of AI and ML. Experts say it is only a mere waiting period till we see a complete take-over by AI and ML in the educational sector. 

According to a survey by Market Research Future (MRFR), there will be a 38 per cent increase in growth in the educational sector by the year 2023. It is important to understand that technological advancement is going to take over the education sector in the future. So, the implementation of AI and ML in the education sector is extremely important to create a great affinity with the coming future. 

AI and ML are filling up the void of real-time content management and feedback responses in many sectors. The educational sector is one of them and chatbots are one such common tool that is giving a basic helping hand to feed real-time data reverting from the websites. Many educational institutions use chatbots and also certain software which enhances their content for better ranking. So generating leads, content management, managing feedback, etc. are key spaces that the educational sector will look forward to in the coming years.

Some ways AI and ML will change and take over the educational sector in the upcoming years:

Increase in Efficiency and Accuracy

Classroom management, scheduling, and other records are stored more efficiently. Recording all the necessary details are more accurate as AI and ML usage eradicates the majority of human error.

Analyzing student and staff behaviour and progress gets convenient

A proper analysis of the students and staff is important to enhance the learning and coexisting experience in the educational sector. The student who is facing difficulties is more accurately analyzed and helped due to their pinpoint record keeping and observation.

Also, much like students, even staff need to analyze their performance in the field so that they rectify their mistakes and work for the betterment of the sector. And the regularity of the maintenance is constant only due to AI and ML.

Enhanced Learning Experience

The learning experience changes when AI and ML are utilized. The sense of personalized learning was envied by students earlier, but now it seems like each system is paying attention to one student at a time. 

Reaching a fair conclusion of the assessments

In the student curriculum, the difficult job is to assess the assessments while rectifying the mistakes and keeping a record of them. With AI and ML, the assignments are distributed fairly and a student’s assignment is analyzed accurately so that the grading system is organized and full of transparency.

Henceforth, AI and ML are just the headstarts of something great. Also, the advancement process is not going to stop anytime soon. Since the beginning of the advancements, the education sector has provided aspirants who are practically and logically skilled for their respective professional fields. The skills are easier to nourish and sharpen when there is the support of advancing technology. Thus, AI and ML will not only help the educational sector to improve but also will help students and staff to sustain themselves with developing times.

The post AI And ML To Take Over The Education Sector In The Upcoming Years appeared first on Artificial Intelligence.

Data annotation is the process of labeling data to make it easy for machines to access it.

Why did humans start making machines? The immediate answer would be to make a mechanical and computerised model that works like humans. Yes, humans wanted machines to imitate whatever they do. The purpose of artificial intelligence is no different. If we look at the things that artificial intelligence-powered machines are doing for us today, most of them try to minimize our work by taking over the routine, time-consuming jobs. In order to make machine learning models advanced, they should be trained with datasets. That is where data annotation makes its debut.

Artificial intelligence and machine learning have changed the way we live. Starting from product recommendations and search engine results to self-driving cars and autonomous drones, everything is powered by artificial intelligence. However, this would be impossible without data annotation. Today, we are building a future where automation and autonomous-powered working is everything. To create such automated applications and machines, the datasets need to be trained properly. However, since the datasets are very huge and the human mode of training won’t help, artificial intelligence companies use data annotation to label the content and use it for machine learning models’ training. By implying data annotation, machine learning models get to be fed with well trained and labelled datasets. In this article, we take you through the basics of data annotation, explain its types, and list the use cases.

• DATA ANNOTATION – OUTSOURCING V/S IN-HOUSE – ROI AND BENEFITS
• A GUIDE TO MACHINE LEARNING: EVERYTHING YOU NEED TO KNOW
• OPERATE MACHINE LEARNING IN MS EXCEL WITHOUT A SINGLE LINE OF CODE

What is data annotation?

In simple terms, data annotation is the process of labelling data to make it easy for machines to access it. Data annotation is specifically important for supervised machine learning as the models rely on labelled datasets to process, understand, and learn from input patterns to arrive at desired outputs.

Data comes in various forms like text, image, video, documents, etc. But such diverse types can’t be fed into a machine learning model without segregating and sorting it according to their varieties. Therefore, data annotation acts as an intermediary tool to mitigate training issues. By using data annotation, companies can train their machine learning models with the right tools and techniques. In a machine learning model, data annotation takes place before the information gets fed to a system. The process is similar to how we teach kids. For example, in order to teach them about a ball, we either show the picture or a real ball. Similarly, data annotation labels the object as ‘ball’ in the dataset and feeds it to the machine learning model. Some of the uses of data annotation are listed as follows,

• While using annotated data to train a machine learning model, the accuracy of its mechanism will be higher.
• Machine learning models trained with annotated data leverages a seamless experience for end-users.
• Even virtual assistants or chatbots use the trained dataset to answer users’ queries.
• In search engine recommendation, a machine learning model trained with annotated data provides comprehensive results.
• Besides helping on large scale, data annotation can help with localized labelling based on geolocations. It locally labels information, images, and other content.

What is human-annotated data?

Despite the sophistication technology is enjoying, they will be nothing without humans help. It is no different while training a machine learning model. Human help big time in making machines learn about the way the world functions. Therefore, data annotation loops humans in the training process to improve performance.

But why is human-annotated data important in machine learning? Humans have a special talent called judgement and hunch, which machines don’t possess. The recent developments in the technology industry are pointing to developing machines that can think like humans. That is where human-annotated data comes into the picture. Human-annotated data introduces subjectivity, intent, and clarification, making machines determine whether a search result is relevant.

Types of data annotation

Text annotation: Today, most companies are moving to automatic models, especially, text-based to power their working system. Owing to the increasing adoption, text annotation has become the centre of attention recently. Text annotation includes a wide variety of annotations like sentiment, intent, and query.

Video annotation: When it comes to video annotation, humans are seen as a good source to train the datasets. For example, companies use human assistance in search engine results. They collect the input from many people in terms of their preferences and promote similar content to others.

Image annotation: Image annotation is very important in training a dataset. Many technologies including computer vision, robotic vision, facial recognition, etc. rely on image annotation to label and interpret image forms. To train the models with image data, metadata must be assigned to the images in form of identifiers, captions, or keywords.

Audio annotation: Audio annotation is quite different from the other types of annotation. Unlike others, audio annotation takes an in-depth step to transcribe and time-stamp the speech data, including transcription of specific pronunciation and intonation.

The post DATA ANNOTATION: CHANGING THE TAILWIND OF ML MODEL TRAINING appeared first on Artificial Intelligence.

Most NLP researchers prioritise the development of deep learning models over the quality of training data. The relative lack of attention results in training data picking up spurious patterns, social biases, and annotation artefacts. 

Data curation is the organisation and integration of data collected from multiple sources. The process involves authentication, archiving, management, preservation for retrieval, and representation.

Her paper laid down the arguments for and against data curation.

Why data curation is important

In her paper, Rogers gives the following arguments in support of data curation:

Social biases: Written text may contain all kinds of social biases based on race, gender, social status, age, and ability. Models may learn these biases, and when deployed in real-world scenarios, they may propagate and further amplify them. This puts minority groups at a significant disadvantage. It’s imperative to select data taking sociocultural characteristics into account and promote fair representation of all social groups.

Privacy: Using personally identifiable information in training data can give rise to privacy and security concerns. For example, a study showed GPT-2 memorised personal contact information even when it appeared only on a few web pages. “Deciding what should not be remembered is clearly a data curation issue,” writes Rogers.

Security: Universal adversarial triggers force models to output a certain prediction. A recently discovered phenomenon, this effect affects the training data, compromising even the robust models. Data curation can help avoid this attack. 

Evaluation methodology: For NLP tasks, the test sample comes from the same distribution as the training samples. There is a possibility of the samples getting overlapped. Curation is necessary to ensure no overlapping takes place.

Progress towards NLU: With rapid scaling, we often lose track of the data on which a model is trained. Without data curation, the models may suffer from one of the following issues:

• Falling prey to common perturbations. For example, linguistic phenomenons such as negations.
• Learning spurious patterns in the data.
• Struggling to learn rare occurrences.

Arguments against data curation

Many experts believe data must be used in their natural form to give an unvarnished output. While there is no problem with this argument, Rogers said, it needs more elaboration. “In that case, the “natural” distribution may not even be what we want: e.g. if the goal is a question answering system, then the “natural” distribution of questions asked in daily life (with most questions about time and weather) will not be helpful,” wrote Rogers. She further added there is still a lot of research work that needs to be done before developers can study the world as it is.

Some developers feel their data is large enough for their training set to encompass the ‘entire data universe’. Rogers said collecting all data is impossible as it will pose legal, ethical, and practical challenges

Meanwhile, many are in favour of developing algorithmic alternatives to data curation. As per Rogers, this is a good possibility; however, having such solutions, in the current scenario, could be a complementary approach to data curation rather than completely replacing it.

A few experts believe data curation is part of the process and should not become a task big enough to forget the original purpose of developing a model. Even though the current deep learning systems are better, they still need to train within the range of the training data, Rogers said.

“A perfect dataset would provide a strong signal for each phenomenon that should be learned. That’s not how language works, so we may never be able to create something like that,” she said. While it may be difficult to achieve perfect solutions, it is always possible to improve the models.

Curation means making a decision about what to include and what to exclude. This can be a daunting task and requires a lot of interdisciplinary expertise, Rogers said.

Wrapping up

“We do want more robust and linguistically capable models, and we do want models that do not leak sensitive data or propagate harmful stereotypes. Whether those goals would be ultimately achieved by curating large corpora or by more algorithmic solutions, in both cases we need to do a lot more data work,” writes Rogers. To achieve this goal, the developers have to overcome interdisciplinary tensions and promote truly collaborative spaces.

The post Plea To ML Researchers: Give Data Curation A Chance appeared first on Artificial Intelligence.

Machine intelligence is the combination of AI and ML

Serving dishes, controlling traffic, performing surgeries on humans – think of these and the first impression is that you cannot do without humans here. The situation now seems to have undergone a 360 degree transformation. Gone are the days when every task that you can think of needed human intervention. Now, you find machines taking up the role of waiters, traffic controllers, educators and what not. One of the greatest achievement is in the field of healthcare sector. Machines are assisting doctors and surgeons while performing medical procedures. We have reached a stage wherein some not so difficult procedures are done by machines themselves without the involvement of humans.

This machine intelligence has truly transformed the way we look at things. This kind of intelligence has made it easy to address issues and problems in every field like never before. The reason why machines are intelligent to the extent that they hold the potential to perform tasks just like humans is because of Artificial Intelligence. It is only by virtue of Artificial Intelligence that we get to see human-like machines and computers. This area will see a lot more advancements in the near future, without a doubt. With AI, machines are capable of interacting in an intelligent way. Contrary to popular belief, it is not because of the fact that machines are able to perform a couple of tasks like humans that makes them intelligent. The story goes beyond all of this.

An intelligent machine, system, hardware or any computer is not intelligent because it is able to perform human-like tasks. It is solely because such machines stand the potential to complete tasks in an unreliable environment. Unlike what they are being asked to do, machines are intelligent if they can judge what’s going around by being able to monitor the environment and then acting accordingly. Just imagine how a person would react to different situations. Same is the case with machines. If a person is able to make the right use of intelligence, it is then that he / she is said to be intelligent. If the similar criteria is followed in case of machines and they are able to react just like humans by making the best use of their intelligence, then that is what constitutes an intelligent machine.

Probably the best examples of intelligent machines are Alexa and Siri. Not forgetting to mention here, how popular they have become over a period. Also, their demand continues to rise – thanks to AI. It is impossible to imagine machines being intelligent without Artificial Intelligence in place. It is solely because of AI that the machines can come up with improved decisions for the company. They do this by accessing information in the best manner possible.

What constitutes Machine intelligence?

When talking about machine intelligence, there are two concepts that are critical and form the base of the origin – Artificial Intelligence and machine learning. A combination of these two is the reason why machines are proactive. These two allow the machines to not just collect the data but also process it to arrive at conclusions. Basis these conclusions, the organizations make decisions. To make machines work human-like, naturally, some aspects of humans will have to be incorporated. Skills like problem solving, learning ability, prioritization, etc. go in the making of machine intelligence. Needless to say, programming has to be the pre-requisite. Also, machines are designed keeping in mind the concept of “deductive logic”. Using this, they are well aware of when they have made mistakes. Learning from this, the machines ensure that the same mistake isn’t committed againin the future.

Though not many skills go into making machines intelligent, the way they handle the situations and tackle problems does come out to be surprising.

It is because of this that companies are inclined towards machine intelligence. They include a set of automation techniques and develop a model that’d help them achieve their goals. This form of intelligence has eased a lot of issues and hence will continue to rule for the years to come.

The post MACHINE INTELLIGENCE IS HERE AT THE TECHNOLOGY SECTOR TO STAY! appeared first on Artificial Intelligence.

A study finds image analysis using machine learning can identify haematological malignancies.

Image analysis is typically used to extract meaningful information from images. It can perform tasks like finding shapes, identifying edges, removing noise, counting objects, etc. for image quality. Recently, a study demonstrated that image analysis utilizing neural networks can help detect details in tissue samples that are difficult to determine with the human eye. Myelodysplastic syndrome (MDS) is a disease of the stem cells in the bone marrow, which affects the maturation and differentiation of blood cells. Diagnosing MDS requires a bone marrow sample to investigate genetic changes in the bone marrow cells.

Annually, some 200 Finns are diagnosed with MDS, which can develop into acute leukaemia. The incidence of MDS globally is 4 cases per 100,000 person years. The syndrome is classified into groups to find out the nature of the disorder in more detail.

In the University of Helsinki study, microscopic images of patients’ bone marrow samples suffering from myelodysplastic syndrome were analysed utilising an image analysis technique based on machine learning. The samples were stained with haematoxylin and eosin (H&E staining), a procedure of routine diagnostics for the disease. The slides were digitised and analysed using computational deep learning models.

The study was published in the Blood Cancer Discovery, a journal of the American Association for Cancer Research. The results can be explored with an interactive tool: http://hruh-20.it.helsinki.fi/mds_visualization/.

With machine learning, the digital image dataset could be assessed to accurately identify the most common genetic mutations affecting the progression of the syndrome, such as acquired mutations and chromosomal aberrations. The higher the number of abnormal cells in the samples, the higher the reliability of the results generated by the prognostic models.

The study uses the data analysis technique to support the diagnosis. One of the greatest challenges of leveraging neural network models is to understand the criteria on which they base their conclusions drawn from data, such as information contained in images. The University of Helsinki study succeeded in determining what deep learning models see in tissue samples when they have been taught to look for, for example, genetic mutations related to MDS. The technique provides new information on the effects of complex diseases on bone marrow cells and the surrounding tissues.

According to Professor Satu Mustjoki, ‘the study confirms that computational analysis helps to identify features that elude the human eye. Moreover, data analysis helps to collect quantitative data on cellular changes and their relevance to the patient’s prognosis.’

Part of the analytics carried out in the study was implemented using the Helsinki University Hospital (HUS) data lake environment, which enables the efficient collection and analysis of extensive clinical datasets.

“We’ve developed solutions to structure and analyse data stored in the HUS data lake. Image analysis helps us analyse large quantities of biopsies and rapidly produce diverse information on disease progression. The techniques developed in the project are suited to other projects as well, and they are perfect examples of digitalizing medical science,” says doctoral student Oscar Bruck.

Ph.D. Olivier Elemento from the Caryl and Israel Englander Institute for Precision Medicine says, “[This] study provides new insights into the pathobiology of MDS and paves the way for increased use of artificial intelligence for the assessment and diagnosis of hematological malignancies.”

The post IMAGE ANALYSIS USING ML IDENTIFIES HAEMATOLOGICAL MALIGNANCIES appeared first on Artificial Intelligence.

Today, AI and ML are transforming the face of education technology.

Today, AI and ML (Artificial Intelligence, Machine Learning) are creating havoc in a number of fields and industries, including education. As per report of Ideamotive, according to Market Research Engine, the global AI in the education market will reach $5.80 billion by 2025 at a compound annual growth rate of 45%.

The following are some of the ways that machine learning and artificial intelligence are transforming the face of education technology:

An Approach to Learning That Is Both Structured and Personalized

The use of machine learning and artificial intelligence has assisted in the development of a more effective and time-saving approach for teachers to follow a clear ride of guidance. This has aided students’ comprehension and has gone beyond the criteria of human intelligence. Many ed-tech companies have started to deploy digital programs to boost the educational experiences, as AI in ed-tech helps respond to students’ specific needs. It helps students in assessing their success on various subjects and keeps track of their input.

Augmented and Virtual Reality

It is one of the most exciting advances in the area of AI and machine learning. Many colleges and universities are using this advanced technology to clarify life-like experiences in disciplines such as biology, astronomy, geology, and others. Students are able to interact with different topics using AR/VR technologies that include animations, pictures, movies, and more. This technology has proven to be the most effective means of assisting teachers and administrators in obtaining extremely accurate subject-oriented experiences.

Choosing the Best Profession

AI and machine learning will assist students in overcoming their dilemmas and predicaments when it comes to choosing the right career direction. Many times, poor decisions on this crucial front have resulted in the futures of millions of talented students being jeopardized. Fortunately, in the future, AI and machine learning will be able to save students from the regrettable pain of self-inflicted career destruction. All of these tools have excellent data mining techniques, which inevitably provide deep insight into students’ interests and despises, as well as their long-term objectives.

For Students with Special Needs, AI and Machine Learning is a Boon

AI and machine learning technology have proved to be an outstanding source of education for students with special needs. Many specially-abled students are encouraged to learn the subject through speech recognition and virtual reality technology, which enable them to effectively and ideally master even the most difficult topics.

What role does AI play in education?

AI is now being used in education, especially in the form of skill development tools and testing methods. When AI educational solutions develop, it is anticipated that AI will be able to help identify voids in teaching and learning, allowing teachers and administrators to do better than ever before.

There are several AI applications in education. Both teachers and students profit from the innovation. The education industry will benefit from AI in a number of ways. Byju’s, Vedantu like e-learning sites, in particular, are offering educational institutions a competitive advantage. It has introduced artificial intelligence (AI) e-learning software to connect with students and have more customized tutorials.

Conclusion

Education will be rapidly accelerated in the future, and learning needs will be even more diverse. Artificial intelligence can be extremely useful in identifying patterns before they take root and rapidly adapting to them.

The curriculum of the future educational institutions will be able to adjust as required. Additional teaching technologies will help students without putting undue pressure on teachers, and educators will be able to use their time and efforts more dynamically.

The post AI AND ML TO FURTHER REVAMP THE ED-TECH SECTOR? HERE’S HOW! appeared first on Artificial Intelligence.

These are heady days in data science and machine learning (DSML) according to Gartner, which identified a “glut” of innovation occurring in the market for DSML platforms. From established companies chasing AutoML or model governance to startups focusing on MLops or explainable AI, a plethora of vendors are simultaneously moving in all directions with their products as they seek to differentiate themselves amid a very diverse audience.

“The DSML market is simultaneously more vibrant and messier than ever,” a gaggle of Gartner analysts led by Peter Krensky wrote in the Magic Quadrant for DSML Platforms, which was published earlier this month. “The definitions and parameters of data science and data scientists continue to evolve, and the market is dramatically different from how it was in 2014, when we published the first Magic Quadrant on it.”

The 2021 Magic Quadrant for DSML is heavily represented by companies to the right of the axis, which anybody who’s familiar with Gartner’s quadrant-based assessment method knows represents the “completeness of vision.” No fewer than 13 of the 20 vendors to make the quadrant’s cut landed on the right side, which indicates active innovation.

Generating new DSML features and exploring new DSML methods is the name of the game in this fast-moving business, Gartner says. “There remains a glut of compelling innovations and visionary roadmaps,” the analysts wrote. “…[V]endors are heavily focused on innovation and differentiation, rather than pure execution. Innovation remains key to survival and relevance.”

The Connecticut-based analyst firm did not sound surprised to conclude that the cloud biggies have moved strongly into the space. “The long-expected gigantic presence in this market of Google and Amazon is now easily felt as they compete with Microsoft for supremacy in terms of DSML capabilities in the cloud,” the analysts write.

However, that does not mean that they are sucking all the air out of the room, as smaller companies have found success in the market, with a few achieving what Gartner termed “hypergrowth.” A few well-established leaders from the previous generation of statistical tools, like SAS, MathWorks, and IBM (SPSS) are also doing well, Gartner notes. In fact, those three vendors are collectively doing better than AWS, Google, and Microsoft when it comes to ability to execute.

The DSML market is young and vibrant, and there is ample revenue and funding opportunities for companies that differentiate themselves on the product side, Gartner says. There is just a “moderate” level of M&A activity at this time, which indicates a growing market. With that said, the vendors who made Gartner’s cut had to prove themselves by meeting certain customer-count and financial performance criteria. And of course, they have to have a product that meets the definition of an DSML platform.

Which begs the question: Just what is an DSML platform? Gartner defines it as a place “to source data, build models and operationalize machine learning,” either by certified, card-carrying data scientists or people who are doing data science work, i.e. citizen data scientists, data engineer, or ML specialists.

Beyond that broad definition, Gartner identified 13 other capabilities that may (or may not) exist in a given DSML platform, including: data ingestion; data preparation; data exploration; feature engineering; model creation and training; model testing; deployment; monitoring; maintenance; data and model governance; explainable artificial intelligence (XAI); business value tracking; and collaboration.

Here’s a brief description of the pros and cons provided for each of the vendors listed in the Magic Quadrant, courtesy of Gartner:

Leaders Quadrant

Databricks Unified Data Platform

Pros: Scalable multi-cloud support; empowerment of data scientists; execution and expansion.

Cons: Lack of support for citizen data scientists; need for governance and responsible AI; growing cloud competition.

Dataiku Data Science Studio

Pros: Support for citizen data scientists; focus on business value; market traction.

Cons: Heavy use of extensions and plugins; emerging story around “XOps” (i.e. unified management of data, ML, models, and platforms); pricing for smaller teams.

IBM Watson Studio on IBM Cloud Pak for Data

Pros: support for multiple personas; composite AI vision; responsible AI and governance.

Cons: scope of auto AI features; doubts about Watson brand; lack of clarity in product-bundling.

MathWorks MATLAB

Pros: Robust composite AI capabilities; integrated domain knowledge; verifiable and reliable ML.

Cons: Interface lacks usability among non-engineers and non-scientists; interpretability of ML models; lack of augmented DSML capabilities.

SAS Viya

Pros: Market understanding and presence; cloud-native architecture and open source integration; automated feature engineering and modeling.

Cons: Perceived high cost; product bundling; marketing strategy.

TIBCO Software (various products)

Pros: Leading edge DSML capabilities; integration of DS and BI/analytics; support for collaboration and applied analytics.

Cons: Limited ModelOps capabilities; lack of support for citizen data science capabilities; financial growth in 2020.

Visionaries Quadrant

AWS (various products)

Pros: Breadth and depth of cloud platform; performance and scalability; data labeling and human-in-the-loop capabilities

Cons: Lack of attention on citizen data scientist; rapid rollout of products and maturity; maturity of on-prem, hybrid, and multi-cloud support

DataRobot Enterprise AI Platform

Pros: Sales strategy and execution; high-touch customer service; successful acquisitions.

Cons: Complexity of product portfolio; resource-heavy onboarding; capability gaps.

Google Cloud AI Platform

Pros: Responsible AI vision and capabilities; research contributions; cohesion and simplification of consolidated products.

Cons: Rapid pace of change; steep learning curve; lack of capabilities for on-prem, hybrid, and multi-cloud deployments.

KNIME Analytics Platform

Pros: Breadth and depth of DSML capabilities; commitment to open source; visual workflow coherence.

Cons: Limitations in enterprise deployments; responsible AI vision; low market traction.

Microsoft Azure Machine Learning

Pros: Strong support for enterprise DS; support for multiple personas; openness and partnerships.

Cons: Requirement of use of other Azure services; immaturity of on-prem, hybrid, and multi-cloud capabilities; lack of support for augmented DSML capabilities.

RapidMiner (various products)

Pros: Support for multiple personas; “clear vision and delivery of aligned features”; expandability and governance.

Cons: Growth rate; average advanced analytics capabilities; academic perception of product.

H2O.ai (various products)

Pros: Vision for value creation; extensive automation; rich AI explainability features (XAI).

Cons: Lack of some data access and data prep features; OEM partner strategy; collaboration and cohesion.

Challengers Quadrant

Alteryx Analytics Process Automation

Pros: Support for multiple personas; product packaging and go-to-market strategy; customer support.

Cons: Changing product portfolio; high cost; lack of innovation.

Niche Players Quadrant

Alibaba Cloud’s Platform for AI (PAI) Studio and Data Science Workshop

Pros: Strong community in China; advanced use-case modeling; and seamless integration.

Cons: Focus on Asia; lack of product vision; narrow usage and focus on professional data scientists.

Altair Knowledge Studio and Knowledge Works

Pros: Ease of use; support for data pipelines; customer satisfaction

Cons: Functional gaps in lineup; limited rollouts in some industries; relatively slow growth.

Anaconda Enterprise

Pros: Trusted and flexible platform; based on open source; culture of collaboration.

Cons: Focus on technical audience; lack of model operationalization functions; runtime stability.

Cloudera Data Platform

Pros: Native Spark on Kubernetes; support for complex data workloads; metadata support for DataOps and MLOps.

Cons: No GUI for development; lack of coherence of products; domain-specific solutions.

Domino Data Lab Data Science Platform

Pros: Support for large, expert teams; mature MLOps capabilities; support for on-prem, hybrid, and multi-cloud.

Cons: Support for small, immature DS teams; low market visibility; open source vision;

Samsung SDS Brightics AI

Pros: Comprehensive ecosystem vision; data access, prep, and visualization; ease of use and collaboration.

Cons: Limited adoption outside of Asia; gaps in product vision; limited capabilities in ModelOps and explainability.

This is indeed a great time to be in the data science and machine learning business. Whether you’re a user of these tools or helping to develop them, the rapid pace of innovation is not only exciting but good for business as a whole.

The post A ‘Glut’ of Innovation Spotted in Data Science and ML Platforms appeared first on Artificial Intelligence.