Posted on

Accelerating Similarity Search on Really Big Data with Vector Indexing (Part II)

Many popular artificial intelligence (AI) applications are powered by vector databases, from computer vision to new drug discovery. Indexing, a process of organizing data that drastically accelerates big data search, enables us to efficiently query million, billion, or even trillion-scale vector datasets.

This article is supplementary to the previous blog, "Accelerating Similarity Search on Really Big Data with Vector Indexing," covering the role indexing plays in making vector similarity search efficient and different indexes, including FLAT, IVF_FLAT, IVF_SQ8, and IVF_SQ8H. This article also provides the performance test results of the four indexes. We recommend reading this blog first.

This article provides an overview of the four main types of indexes and continues to introduce four different indexes: IVF_PQ, HNSW, ANNOY, and E2LSH.

Posted on

Dodge Adversarial AI Attacks Before It’s Too Late!

Introduction

In this tech-oriented world where a number of hackers and technological advancements are emerging in parallel to each other, artificial intelligence has made big strides recently in understanding languages. Contrary to this, artificial intelligence can still suffer from potentially dangerous and alarming sorts of algorithmic insight. Research depicts how AI algorithms that parse and analyze algorithms can be tricked and deceived by precisely crafted phrases. A sentence that might seem appropriate to you may have the strange ability to dodge the AI algorithm. 

It is estimated by the expert community that by the year 2040, artificial intelligence will reach the capability to perform all the intellectual functions of human beings. This might seem frightening but with the few techniques outlined in this teachable, you will radically grow your possibilities of survival when encountering artificial intelligence. 

Posted on

Revitalizing OCR Using Innovative AI and Deep Learning Algorithms

Introduction

In this digital-oriented age, technology is advancing in such a way that it is paving the way for information extraction from handwritten documents or scanned images called Optical character recognition (OCR) data extraction. Thankfully, OCR technology has a wide range of applications to automate and enhance business operations. OCR technology allows data extraction from bank statements, product sheets, passports, contracts, receipts, invoices, utility bills, and a variety of other documents. 

In 2020, the global OCR market size hit the figure of USD 7.46 billion and it is expected that from 2021 to 2028, the market size will expand at a Compound Annual Growth Rate(CAGR) of 16.7%.  No doubt OCR technology performs accurate and reliable data extraction and plays a crucial role in financial infrastructures, insurance claim processing, legal and logistic documentation, but OCR systems cannot perform well with unstructured documents. IDP utilizes numerous AI technologies to pre-process, extract and post-process information to deal with these OCR shortcomings.  

Posted on

The Top 5 Big Data Applications in the Healthcare Industry

In this modern era of leveraging technology, the enhancement of healthcare sectors is crucial especially during the pandemic of COVID-19. Technological advancements can either make or break the future of healthcare and can control the second wave of coronavirus. One method which can be acquired to make healthcare more efficient, accurate, and affordable is by utilizing big data. 

Big data has completely revolutionized the way data is analyzed, managed, and leveraged across numerous industries. Noticeable sectors where data analytics is making prominent changes in healthcare. It is estimated that the global big data in the healthcare market will tend to reach $34.27 billion by the year 2022 at a CAGR of 22.07%. Moreover, big data in the healthcare market is expected to bypass the figure of $68.03 billion by the year 2024. 

Posted on

Innovative Algorithms Are Assisting AI Systems To Escape From ‘Adversarial’ Attacks

Introduction

In this modern and innovative world, individuals are capable enough to get what they see. The role of artificial intelligence would be simultaneously straightforward in that case. Artificial intelligence is one of the most famous data-driven technologies emerging at a swift pace, accommodating the whole world. There would be no surprise in saying that the market size of artificial intelligence is growing dramatically and will reshape the dimensions of technological advancements in the upcoming future. In 2019, the market size of artificial intelligence was estimated at $27.23 billion. This figure projects that the market size will value AI at $266.92 billion by 2027.  

Let us consider the collision avoidance system in self-driven cars. An AI system can directly map an input to an appropriate action if visual input to on-board cameras is entirely trusted i.e. steer left, steer right or go straight continuously in order to dodge any ramblers that cameras notice on the road. But what if the camera is manipulated or slightly shifts images by a few pixels? The car might take potentially unnecessary and dangerous actions if it starts trusting adversarial inputs blindly. 

Posted on

Why Python Is Best for Machine Learning

Today, most companies are using Python for AI and Machine Learning. With predictive analytics and pattern recognition becoming more popular than ever, Python development services are a priority for high-scale enterprises and startups. Python developers are in high-demand — mostly because of what can be achieved with the language. AI programming languages need to be powerful, scalable, and readable. Python code delivers on all three.

While there are other technology stacks available for AI-based projects, Python has turned out to be the best programming language for this purpose. It offers great libraries and frameworks for AI and Machine Learning (ML), as well as computational capabilities, statistical calculations, scientific computing, and much more. 

Posted on

How AI Is Saving Lives and Stopping Human Trafficking

How AI Is Saving Lives and Stopping Human Trafficking

Human trafficking is a horrific crime that impacts between two and four million victims. The impact of modern-day slavery is far-reaching and affects families across the globe. Social scientists, developers, and law enforcement are working together to cut down on the number of people victimized by human trafficking.

Everyone involved was at a loss when trying to track down and arrest these criminals. Worse yet, the trafficker would likely only get charged with one crime, even if they were running a trafficking empire.

Posted on

What Developers Need to Know About Machine Learning in the SDLC

Machine learning

To learn about the current and future state of machine learning (ML) in software development, we gathered insights from IT professionals from 16 solution providers. We asked, "What do developers need to keep in mind when using machine learning in the SDLC?" Here's what we learned:

You might also like:  How Machine Learning Will Affect Software Development

Fundamentals

The biggest issue for ML is viewing it as an omnipotent savior of the SDLC, thereby negating the need to adhere to traditional SDLC design and protocol. ML can greatly improve efficiency and allow developers to better allocate their time to actions that require human input. It cannot, however, completely take the place of conscientious, diligent and thoughtful software planning, design, development, and version control.