Sat.Feb 10, 2018

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The Most Demanded Data Science and Machine Learning Jobs in 2018

CTOvision

While Information Technology fields in general have healthily grown in the past decade, there's an emerging sector in the market that's taking off in ways unrivaled by traditional computer science. Machine learning and data science are two exciting sections of tech where job growth is phenomenal as new and more advanced techniques to collect, store, […].

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Technostress is killing productivity. Culture is the cure.

ComputerWorld IT Management

Technology changes fast. With each new change, employees are forced to adapt. That process of adapting is painful — physically and psychologically. That’s why we seem to get a new health problem related to technology every few years. I want to tell you about the Mother of All technology-related health problems: technostress. But first, a history lesson.

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The Digital Board’s Digital Playbook

Future of CIO

The advanced board is the mastermind behind digital transformation because it plays a crucial role in setting tones for the digital paradigm shift, making good policies for the management to follow, and creating digital principles for inspiring learning and creativity. They co-develop the digital vision and oversee the business strategy, and recommend comprehensive digital playbooks to guide the business through the journey of digital transformation.

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The “PURE” Traits of Digital Innovators

Future of CIO

Innovators have many traits such as vision, passion, determination, dedication, confidence, disobedience, and resilience. Innovators are not compromised with mediocrity and conventional wisdom or get less polluted by negative vibes. They can focus on thinking positively, creatively and being creative as a way of thinking, of imagining, of expression, of perceiving things, of inventing, of inspiring, etc.

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Peak Performance: Continuous Testing & Evaluation of LLM-Based Applications

Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage

Software leaders who are building applications based on Large Language Models (LLMs) often find it a challenge to achieve reliability. It’s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.