The COVID crisis has skyrocketed the applications of artificial intelligence -- from tackling this global pandemic, to being a vital tool in managing various business processes. Despite its benefits, AI has always been scrutinised for its ethical concerns like existing biases and privacy issues. However, this technology also has some significant sustainability issues – it is known to consume a massive amount of energy, creating a negative impact on the environment. As AI technology is getting advanced in predicting weather, understanding human speech, enhancing banking payments, and revolutionising healthcare, the advanced models are not only required to be trained on large datasets, but also require massive computing power to improve its accuracy. Such heavy computing and processing consumes a tremendous amount of energy and emits carbon dioxide, which has become an environmental concern. According to a report, it has been estimated that the power required for training AI models emits approximately 626,000 pounds (284 tonnes) of carbon dioxide, which is comparatively five times the lifetime emissions of the average US car.
In a letter to congress sent on June 8th, IBM's CEO Arvind Krishna made a bold statement regarding the company's policy toward facial recognition. "IBM no longer offers general purpose IBM facial recognition or analysis software," says Krishna. "IBM firmly opposes and will not condone uses of any technology, including facial recognition technology offered by other vendors, for mass surveillance, racial profiling, violations of basic human rights and freedoms, or any purpose which is not consistent with our values and Principles of Trust and Transparency." The company has halted all facial recognition development and disapproves or any technology that could lead to racial profiling. The ethics of face recognition technology have been in question for years. However, there has been little to no movement in the enactment of official laws barring the technology.
Detroit's police chief admitted on Monday that facial recognition technology used by the department misidentifies suspects about 96 percent of the time. It's an eye-opening admission given that the Detroit Police Department is facing criticism for arresting a man based on a bogus match from facial recognition software. Last week, the ACLU filed a complaint with the Detroit Police Department on behalf of Robert Williams, a Black man who was wrongfully arrested for stealing five watches worth $3,800 from a luxury retail store. Investigators first identified Williams by doing a facial recognition search with software from a company called DataWorks Plus. Under police questioning, Williams pointed out that the grainy surveillance footage obtained by police didn't actually look like him.
On Tuesday, a number of AI researchers, ethicists, data scientists, and social scientists released a blog post arguing that academic researchers should stop pursuing research that endeavors to predict the likelihood that an individual will commit a criminal act, as based upon variables like crime statistics and facial scans. The blog post was authored by the Coalition for Critical Technology, who argued that the utilization of such algorithms perpetuates a cycle of prejudice against minorities. Many studies of the efficacy of face recognition and predictive policing algorithms find that the algorithms tend to judge minorities more harshly, which the authors of the blog post argue is due to the inequities in the criminal justice system. The justice system produces biased data, and therefore the algorithms trained on this data propagate those biases, the Coalition for Critical Technology argues. The coalition argues that the very notion of "criminality" is often based on race, and therefore research done on these technologies assumes the neutrality of the algorithms when in truth no such neutrality exists.
Obviously, the methods of past years have ceased to be effective. Even Fraud Detection with AI and Machine Learning is neither a magic pill nor an absolute guarantee of protection. However, nothing better was invented at the moment, so it makes sense to learn how ML solutions and fraud detection analysis can make your business more secure, and your customers more confident in your services. The very concept of detecting fraud using machine learning is based on the idea that legitimate and illegal actions have different characteristics. Moreover, these signs can be completely invisible to the human eye. The machine learning system for recognizing fraud proceeds from its knowledge of the legitimate operation, compares this knowledge with events occurring in real-time and draws a conclusion about the validity or illegality of a certain action.
Training a machine learning model requires a large quantity of high-quality data. One way to achieve this is to combine data from many different data organizations or data owners. But data owners are often unwilling to share their data with each other due to privacy concerns, which can stem from business competition, or be a matter of regulatory compliance. The question is: how can we mitigate such privacy concerns? Secure collaborative learning enables many data owners to build robust models on their collective data, but without revealing their data to each other.
BPU Holdings is a global company, headquartered in Korea that pioneers in the development of Artificial Emotional Intelligence (AEI). The mission of the company is to generate the most advanced, secure usable, and innovative Artificial Emotional Intelligence technology in the world. BPU has developed the first Artificial Emotional Intelligent (AEI) platform -- AEI Framework, which emulates how people think and feel. BPU improves the human condition by offering rigorous tools to improve emotional intelligence. Tracking and handling emotions enable the management of professional and interpersonal relationships, empathetically and judiciously.
I've spent the last few months preparing for and applying for data science jobs. It's possible the data science world may reject me and my lack of both experience and a credential above a bachelors degree, in which case I'll do something else. Regardless of what lies in store for my future, I think I've gotten a good grasp of the mindset underlying machine learning and how it differs from traditional statistics, so I thought I'd write about it for those who have a similar background to me considering a similar move.1 This post is geared toward people who are excellent at statistics but don't really "get" machine learning and want to understand the gist of it in about 15 minutes of reading. If you have a traditional academic stats backgrounds (be it econometrics, biostatistics, psychometrics, etc.), there are two good reasons to learn more about data science: The world of data science is, in many ways, hiding in plain sight from the more academically-minded quantitative disciplines.
We are seeing more references to machine learning in how Google is ranking pages and other documents in search results. That seems to be a direction that will leave what we know as traditional, or old school signals that are referred to as ranking signals behind. It's still worth considering some of those older ranking signals because they may play a role in how things are ranked. As I was going through a new patent application from Google on ranking image search results, I decided that it was worth including what I used to look at when trying to rank images. Images can rank highly in image search, and they can also help pages that they appear upon rank higher in organic web results, because they can help make a page more relevant for the query terms that page may be optimized for.
With the emergence of incredibly powerful machine learning technologies, such as Deepfakes and Generative Neural Networks, it is all the easier now to spread false information. In this article, we will briefly introduce deepfakes and generative neural networks, as well as a few ways to spot AI-generated content and protect yourself against misinformation. I have many elderly relatives and some middle-aged relatives that just aren't well-versed with technology. Some of these people believe nearly anything they read, or at least believe it enough to share it on social media. While that doesn't sound so bad, it depends on what you are sharing.