The Robotics Market is expected to reach US$75.3 billion in revenue by 2026

Robotics market

Robotics with its higher success rate has redefined the landscape of technology across the globe. Robotics brings together advanced computer technology providing the world with high-class automation. According to Analytics Insight, the global robotics market is predicted to witness a remarkable increase from US$42.2 billion in 2021 to US$75.3 billion in 2026, registering a CAGR of 12.3%.

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Longtime VC, and happy Miami resident, David Blumberg has raised a new $225 million fund

David Blumberg

Blumberg Capital, founded in 1991 by investor David Blumberg, has just closed its fifth early-stage venture fund with $225 million, a vehicle that Blumberg says was oversubscribed — he planned to raise $200 million — and that has already been used to invest in 16 startups around the world (the firm has small offices in San Francisco, New York, Tel Aviv, and Miami, where Blumberg moved his family last year).

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AI is expected to reach US$360.36 billion in revenue by 2028

AI infographic

As the field of artificial intelligence is progressing with spontaneous growth, Analytics Insight estimates artificial intelligence to reach US$ 360.36 billion in revenue by the year 2028, with a CAGR of 40.2% from 2021. The AI market is inclusive of a wide array of applications such as natural language processing, robotic process automation, and machine learning.

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The responsibilities of AI-first investors

AI Investors

Investors in AI-first technology companies serving the defense industry, such as Palantir, Primer and Anduril, are doing well. Anduril, for one, reached a valuation of over $4 billion in less than four years. Many other companies that build general-purpose, AI-first technologies — such as image labeling — receive large (undisclosed) portions of their revenue from the defense industry.

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Understanding the role and attributes of Data Access Governance in Data Science & Analytics

Data access governance

Data scientists and business analysts need to not only find answers to their questions by querying data in various repositories, but also transform it in order to build sophisticated analysis and models. Read and write operations are at the heart of the data science process and are essential to helping them make quick and highly informed decision-making. It is also an imperative capability for data infrastructure teams that are tasked with democratizing data while complying with privacy and industry regulations.

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Data science hasn’t fixed its huge gender pay gap

AI Diversity

While 64% of employees in data science, AI, and machine learning took part in training or obtained new certifications over the past year, the average change in compensation was $9,252 — an increase of about 2.25% annually. That’s one of the findings in O’Reilly’s 2021 Data/AI Salary Survey, which took a look at job satisfaction and salaries in data science fields experiencing a shortage of qualified employees. The results suggest that data and AI professionals are among the most driven employees when it comes to upskilling — and have a clear desire to learn.

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Bias in AI

Bias in AI

As AI becomes increasingly important to many fields, the racist and prejudiced views of our American society infiltrate its systems in ways that lead to algorithmic bias. The term artificial intelligence was coined in 1956 at a conference at Dartmouth College where it was defined as the “science and engineering of making intelligent machines” by John McCarthy. However, the field has grown rapidly since then and various definitions have followed suit.

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Finally, Tech Giants are turning down Unethical AI Projects

Tech giants

The pros of artificial intelligence technology have always been followed up with the cons of leveraging these technologies. With the launch of every new system that requires users to share personal and biometric data, a school of thought has emerged voicing the ethical and privacy concerns of using the systems. A recent investigative report by Reuters sheds light on how the three US tech giants — Google, IBM and Microsoft — have been resisting and turning down projects on account of ethics concerns. 

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Hyperautomation puts focus back on creating Business Value

Hyper automation

It has been commonplace for the IT industry to tout the mantra of automating everything for some time. The cloud has accelerated that imperative for automation more than ever before. Yet, many organizations still have no strategy for how they will use automation within the business.

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Agile Data Science: What does it mean? How to manage Data Science Projects


“What does agile data science mean?” you might be asking. In one word: agile! Agile is a methodology that has been embraced by many industries, including data science. It’s time to get agile with your data science projects and start increasing efficiency and decreasing costs. This blog post talks about what agile data science is, how it can help you manage your projects better, and tips around how it can be used in the context of your company’s culture.

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Cogito Artificial Intelligent (AI) Software - An Ethics Case Study

AI ethics in voice interactions

Cogito software is an Artificial Intelligence (AI) system that provides call center agents with real-time feedback and conversational guidance to enhance customer experience. Backed by behavioural science, Cogito is unique because it gives human call center agents live suggestions from concepts such as their empathy levels and pace. It is a human-AI interaction software that has proven successful for call centers in the healthcare and finance industries. The Markkula Framework is used in analyzing the Cogito Software from an ethical perspective by applying Consequentialism, Deontology and Virtue Ethics theories. In this paper, I focus on the ethical perspectives of the Cogito software.

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Overcoming AI’s transparency paradox

Transparency paradox

AI has a well-documented but poorly understood transparency problem. 51% of business executives report that AI transparency and ethics are important for their business, and not surprisingly, 41% of senior executives state that they have suspended the deployment of an AI tool because of a potential ethical issue. In order to fully understand why AI transparency is such a challenging issue, we first ought to reconcile with some common misconceptions and their realities within AI transparency to gain a better view of the problem and the best way to address transparency within the context of the current ML tools in the market. 

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