Survey finds 96% of execs are adopting ‘offensive AI’ against cyberattacks

Darktrace report

“Offensive AI” will enable cybercriminals to direct attacks on enterprises while flying under the radar of conventional, rules-based detection tools. That’s according a new survey published by MIT Technology Review Insights and Darktrace, which found that more than half of business leaders believe security strategies based on human-led responses are failing.

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Delivering AI/ML without proper Dataops is just wishful thinking!

DataOps Team Process

Given the iterative nature of AI/ML projects, having an agile process of building fast and reliable data pipelines (referred to as DataOps) has been the key differentiator in the ML projects that succeeded (unless there was a very exhaustive feature store available which is typically never the case).

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Why you should use Continuous Integration and Continuous Deployment in your Machine Learning projects

CI-CD - Continuous Integration/ Continuous Delivery

Continuous integration (CI), continuous delivery (CD) and continuous testing (CT) are at the core of Machine Learning Operation (MLOps) principles. If you’re a data scientist or machine learning engineer or an IT business leader investing in data science teams, and willing to extend their ML capabilities. MLOps might be the next step that delivers significant value to your business, speeding up development and implementation phases for any machine learning project.

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Reverse Logistics Company uses AI to find Value in Retail

Reverse Logistics Ecosystem

While “Retail Apocalypse” would no doubt make a great sequel to the iconic movie “Apocalypse Now”. While most fingers point at Amazon (AMZN) as the primary culprit behind the destruction of the American strip mall Main Street America, the retail apocalypse is another example of evolve or die. Margins are tight, so companies looking to survive are searching for ways to eke out value anywhere along the supply chain. One emerging avenue is through something called reverse logistics. 

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Do companies need a Chief AI-Ethics Officer?

Ethical workflow

The world we live in is becoming more and more data-driven. This is causing companies to make more and more use of AI techniques such as machine learning and deep learning. The task of the Chief AI Ethics Officer (CAIEO) should not be primarily technical. Instead, it should sensitize data scientists, machine learning engineers, and developers to ethical issues. The whole process should be firmly integrated into the respective process models and phases.

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Follow the Money March 2021: 15 funded Machine Learning companies

Slice of the Pye: A funding for March 2021

March 2021 latest funding covering artificial intelligence, machine learning, robotics, and innovation.

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Artificial Intelligence is playing a big role in Fraud Investigation

AI investigates fraud

New and complex challenges behind managing the fraud investigation are a strenuous task in itself. Well, it doesn’t end there. Cross-border probe adds to the already existing complexity. Such an investigation could highlight bribery, corruption, data breach, conflict of interest, fraud in financial reporting and IP theft, to name a few.

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5 common ways companies use RPA to enhance Document Processing

Paper plane

With all the hype around robotic process automation (RPA), it can be helpful to remember that the vast majority of the data running through RPA automations originates or terminates with a document. In fact, I’d estimate that about 80% of RPA automations fall into this category.

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OpenAI is open for business, GPT-3 powers the Next generation of Apps

OpenAI Logo

OpenAI’s GPT-3 is well known in the machine learning world for their human like writing, where it is powering 300 applications, supporting GPT-3–powered search, conversation, text completion, and other advanced AI features through their API.  As reference, here is a shortlist of example OpenAI’s use cases in how developers and applications are using GPT-3.

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Optimizing Machine Learning: MLOps and its significant benefits

Machine woman with orb

MLOps ensures effective lifecycle management of ML models. Machine learning operations (MLOps) is a procedure that has recently entered the dictionary of technology organizations. More or less, MLOps is a method of optimizing the work process of data science and machine learning teams. It’s like DevOps from numerous points of view, additionally focusing on automation, continuous processes for testing and delivery, and collaboration between teams.

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Key NoOps insights from DevOps Leaders

Man set to run

NoOps is coming, in some form or another. Whether you have homegrown DevOps or outsourced DevOps, it’s time to realize that a simpler way is coming: NoOps automation. We spoke with a group of IT pros to get their perspective and insights, including James Rutt, CIO, The Dana Foundation; Rahul Subramaniam, CEO, DevFactory; and Mark Hahn, director of DevOps and cloud strategies, Ciber Global.

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Top AI companies in the World creating massive Disruption

AI graphic

Artificial intelligence is invading each industry, permitting vehicles to drive without drivers, helping doctors with clinical diagnoses, and imitating the manner in which humans talk. Yet, for every one of the legitimate and exciting ways, it’s changing the tasks computers can play out, there’s a lot of hype, as well. As artificial intelligence has become a strong power in business, today’s top AI companies in the world are leaders of this growing technology.

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What are the market drivers, restraints and opportunities of AI?

Drivers, restraints, opportunities of AI

Companies across the world are increasingly turning to Artificial Intelligence implementation for their smooth business operations. The technology has become very constructive in performing a wide range of tasks that are complex and cumbersome for humans, bolstering employee productivity. The use of AI can also aid enterprises to combat cybersecurity risks and thwart them from potential data breaches. With the growing capabilities of AI across diverse business functions, here are the key driver, restraints  and opportunities AI presents.

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