Artificial intelligence is widely viewed as likely to usher in the next big step-change in computing, but a recent interesting development in the field has particular implications for open source. It concerns the rise of "ethical" AI. It's long been accepted that the creators of open-source projects cannot stop their code from being used for purposes with which they may not agree or even strongly condemn—that's why it's called free software. What exactly does the rise of "ethical" AI imply for the Open Source world, and how should the community respond?
A large scale adoption of blockchain by corporates and others has long time been hindered by the lack of options. But that is changing. When interacting with the blockchain they have now two options. They can either set up their node directly, thereby removing the “invisibility cloak” of blockchain. Or they can decide to let someone else do that for them. And here comes BaaS or Blockchain-as-a-Service in scope. BaaS or Blockchain-as-a-Service is comparatively a new blockchain technology, that can be easily integrated in existing corporate infrastructures.
Artificial intelligence (AI) and automation are becoming the go-to options for addressing the troubles of modern-day AML and KYC. The biggest draw is AI’s ability to interpret, synthesize and correlate vast amounts of data, a game-changing contribution to the ongoing battle against financial crime. When building an AI strategy, it’s important to identify expected outcomes and evaluate how AI can be applied to achieve specific goals. Here are the four pieces of lowest hanging fruit in AI for KYC that will bring the ROI.
Matplotlib is one of the most used python libraries to visualize and explore data. It enables you to draw different types of graphs, like line, scattered, bar, and so on. You could say that it is the de-facto standard library for plotting data with python. Matplotlib can be used in Python scripts, Python and IPython shell’s, Jupiter notebook’s, web application servers, and four graphical user interface toolkits. It enables you to generate a visual representation of data with just a few lines of code.
When it turns out that the decisions that are based on a model are bad, it evokes a few obvious questions: Is my model broken? Is my data pipeline broken? Has the thing I’m modeling changed? Answering these questions should be no harder than looking at a dashboard. People tend to rebuild from scratch instead of reverse engineer. Here are the 5 important things that will keep your work robust and relevant, while saving you lots of time that would otherwise be wasted on unnecessary operational firefighting.
In computer-aided processing of natural languages, shall the concept of natural language processing give way to natural language understanding? Or is the relation between the two concepts subtler and more complicated that merely linear progressing of a technology? Though sometimes used interchangeably, they are actually two different concepts that have some overlap. NLP and NLU are opposites of a lot of other data mining techniques. In this post, we’ll scrutinize over the concepts of NLP and NLU and their niches in the AI-related technology.
The term Big Data has been around 2005, when it was launched by O’Reilly Media in 2005. However, the usage of Big Data and the need to understand all available data has been around much longer. While it looks like Big Data is around for a long time already, in fact Big Data is as far as the internet was in 1993. The large Big Data revolution is still ahead of us so a lot will change in the coming years. Let the Big Data era begin!
Assuring compliance and achieving consensus with all parties involved is easier with blockchain. This ensures that all and any operation take place smoothly without any obstructions and hassle free. Blockchain brings out of the box solutions to the traditional challenges faced by automotive industry. With the right integration, the possibilities to achieve maximum potential are high for the automotive sector. Let’s get an idea of how blockchain will help automotive sector that is involved with multiple operations and parties.
We used to build confidence in technology via testing and certification. In order to be convincing to your customers, CEO, board, employees, and social responsibility leaders, testing and an ethical framework will be required. While ethical frameworks can seem daunting, there are simple ways to begin. Autonomous and intelligent systems (A/IS) deployment is accelerating, and managing the risks will be fundamentally different from previous technology waves. Technology adoption in business is routine. But A/IS is fundamentally different from prior generations of technology.
With the incorporation of smart and intelligent methodologies in financial management, the financial advisory industry is progressing towards getting entitled as smart financial services. This demand also is largely promoting growth in the global smart advisor market. There are many technologies that are supporting smart financial advisors in the smart advisors market. One such prominent technology is artificial intelligence or AI. The technology is making dramatic growth of efficiency and quality in the advisory business.
The old way of doing system architecture will not disappear entirely, but it is already past time we started thinking about how to improve the efficiency of our system architecture practices so they better support today’s rapidly evolving business climate. The next major effect of networks on the evolution of system architecture was the desire to integrate systems. It did not take long to realize that entering the same data into different systems was time-consuming and error-prone, so we began to try and integrate systems so they could share data.
In farming, AI is usually short for “artificial insemination.” But a different kind of AI, artificial intelligence, is showing great promise in solving some of agriculture’s most significant challenges, from the need to increase productivity and profits to overcoming labor shortages to protecting the environment. AI can help smaller farms be more profitable by scrutinizing plant data to create a “profit map” that tells farmers the most efficient ways to use a field to maximize profit and yield. Each step, from knowing when to plant, to all aspects of crop care, to knowing when to harvest can be automated.
Reinforcement learning, the special AI technique is considered by many the holy grail of artificial intelligence, because it can create autonomous systems that truly self-learn tasks without human intervention though things are a bit more complicated in reality. While machine learning and its more advanced subset deep learning, can solve many problems that were previously thought to be out of bounds for computers, they are dependent on vast amounts of quality, annotated training data. This makes their application limited in domains where labeled data is scarce. This is where reinforcement learning comes into play.
As more and more systems leverage ML models in their decision-making processes, it will become increasingly important to consider how malicious actors might exploit these models, and how to design defenses against those attacks. There’s a continual arms race between attacks and defenses. So what’s an average ML practitioner to do, who likely doesn’t have time to stay on the very cutting of ML security literature? The purpose of this post is to share some of my recent learnings on this topic.
Increased access to vast amounts of information, social media, and ubiquitous computing makes it seem as if the complexity of our world is increasing faster than we can comprehend it. But technology is not driving complexity; it is only making complexity more visible. Trying to eliminate complexity is an impossible task, and traditional approaches to enterprise architecture have proven ineffective in dealing with it. By thinking about enterprise architecture in a new way, we can make that complexity work for us, and harness emergent behaviors to help achieve an organization’s goals.
Most information out there in the process of learning the ins-and-outs of machine learning is technical and aimed at developers or data scientists. I thought an explanation from a non-technical person might be of interest. AI and machine learning are fascinating but can be tricky at times. Machine learning is the branch of AI that explores ways to get computers to improve their performance based on experience. There are many different models that can be used in machine learning but they are typically grouped into three different types of learning.
IT as a whole has really moved from the things that help the business do day-to-day jobs, to becoming the engine that actually drives the organization. Often, new products and services can’t be launched without IT, and it’s also becoming the point that IT is the product or service being launched. Nearly two-thirds of CIOs say that driving revenue through the creation of new products and services is among their responsibilities today. CIOs offer seven steps to shifting to revenue-driven IT.
In recent years, the development of massive computing and storing capacities in the hand of a few internet juggernauts led to the rise of the cloud economy. Companies of all sizes have been moving their mission-critical servers and operations to the data centers. On the face of it, the development of Infrastructure as a Service (IaaS) should be good news for the state of cybersecurity. In this context, it is easy to believe that moving to the cloud could mean solving many of your cybersecurity issues.
Here is advice on getting started with doing data analysis in Python and I thought it might benefit others if published here. This is for someone new to Python that’s looking for the easiest path from zero to one. Here’s a quick summary of the important libraries you’ll interact with frequently. You will likely always need to refer to the documentation for whatever library you’re using, so just keep it open in your browser.
Machine learning is different than other technological advancements; it is not a plug and play solution, at least not yet. Machine learning can be used to tackle a lot of situations and each situation requires a specific data set, model, and parameters to produce valuable results. Most businesses recognize that machine learning can generate exceptional value but many still wonder how, in what specific areas, and if the time is right to integrate it within their data strategy. Today, we explore what questions you should be asking to know if machine learning is right for your business.
Every organization has realized that data is an asset and utilizes crucial data in multiple applications. In data monetization, blockchain can be a major contributor due to its advanced applications and decentralized nature. With the help of blockchain, data monetization will be accessible to consumers soon. Using blockchain-based data monetization, consumers can monetize and negotiate the value of their data. Also, consumers can control which data can be collected by organizations to ensure data security and privacy.
The IOT has taken on a greater level of importance in a wide range of industries. Those who have been monitoring these effects have been paying close attention to the world of healthcare. The risks that are associated with providing care are reduced and so are the costs. The IOT into the medical sector has been a boon for all parties involved. App development companies and businesses have already been making major strides in this regard. Elderly patients and those who require around the clock supervision are benefiting immensely.
Despite a great deal of lip service and a small amount of capital invested, most corporations are still not data-driven, nor do they use machine learning (ML) and artificial intelligence (AI) to guide their strategic investments in business models. Companies are finally embracing analytics, but still have shown little appetite to be data driven, let alone use ML and AI to help them understand the key drivers of value in today’s highly competitive environment: capital allocation and business model design.
Many organizations are realizing the value of their data so they are beginning to treat their data as a company asset; hence, the rise of the Chief Data Officer (CDO). Data provided from IT service management reports and metrics will be vital information for the CDO as he/she defines strategy for new technology, process, policy, security, and IT architecture. ITSM managers should expect the CDO role to have a direct impact on how IT service management will be implemented, delivered, measured, and most importantly, integrated with other IT solutions within the organization.