The Fourth Industrial Revolution, or Industry 4.0, entails using the most up-to-date versions of technologies such as AI, IoT, cloud computing and big data within industrial environments and operations. For context, the First Industrial Revolution began in the latter part of the 18th century when mechanization from steam and waterpower was revolutionary. Then came the Second Industrial Revolution, which saw the advent of electrical power and mass production systems. Finally, the 20th-century Third Industrial Revolution introduced computers to business processes. The current level of digitization in industries such as manufacturing, healthcare, finance and agriculture is at a level that was once considered futuristic.

At PitchBook, we are always looking forward. We continue to innovate, evolve and invest in ourselves to bring out the best in everyone. We're deeply collaborative and thrive on the excitement, energy and fun that reverberates throughout the company. Our extensive mentorship, education and training programs help us create a culture of curiosity that pushes us to always find new solutions and better ways of doing things. The combination of a rapidly evolving industry and our high ambitions means there's going to be some ambiguity along the way, but we excel when we challenge ourselves. We're willing to take risks, fail fast and do it all over again in the pursuit of excellence.

Part of Speech (POS) Tagging is an integral part of Natural Language Processing (NLP). The first step in most state of the art NLP pipelines is tokenization. Tokenization is the separating of text into "tokens". Tokens are generally regarded as individual pieces of languages – words, whitespace, and punctuation. Once we tokenize our text we can tag it with the part of speech, note that this article only covers the details of part of speech tagging for English.

Tecnotree, the global leader of Digital Business Support Systems, announced the launch of its 5G enabled Digital Online Charging System (DOCS) solution – a cloud-native microservices-based convergent charging platform designed to cater to both current and futuristic monetization use-cases of Communication and Digital Service Providers. "DOCS is going to be the cornerstone for CSPs to monetize the 5G investments as it can also be implemented to cater to specific 5G services with co-existing deployment models along with your current Online Charging Systems." With flattening ARPUs (Average Revenue Per Unit) and declining loyalty, compounded by an explosion in data consumption, Communication Service Providers (CSPs) are under tremendous pressure to offer appealing and hyper personalized services to acquire and retain customers. Also, the technology advancements have widened the scope of CSPs to focus on new business avenues of digital services and IoT/Enterprise services to fight the disruptions and to stay relevant in the Retail and Enterprise Segment. Tecnotree DOCS system is designed to support existing and futuristic monetization requirements of CSPs across the verticals and industries.

The last few years have seen the proliferation of AI ethics principles and guidelines. Public sector agencies, AI vendors, research bodies, think tanks, academic institutions and consultancies have all come up with their own versions. They can all be distilled into four core principles: fairness, accountability, transparency and safety. AI ethics provides valuable inputs for an organization's AI strategy. It gives an organization a handle on the acceptable use of AI and even determines whether an AI system is fit for specific purposes.

Logistics giant Deutsche Post DHL says deployment of an AI-powered internal career marketplace has started to allow its half a million-plus global employee base to take charge of their own career paths. The company also claims that the technology is encouraging team members to build personal profiles that showcases their skills, helping them quickly find relevant training tools to fill skill gaps. The new system - delivered as part of what the corporation sees not as old-style'learning and development', but more modern'learning and growth' - is also claimed to support retention through internal career progression. It is also seen as boosting productivity, as employees feel more supported and empowered. The tech - from AI-powered people experience platform supplier Cornerstone - was also able to do in less than five minutes, what an average two years of learning and development (L&D) effort had been unable to: achieve 85% accuracy of skills categorization, even from a non-customized version.

Let's all agree: the pandemic has reshaped the global supply chain. Multiple lockdowns paired with temporary trade restrictions and workforce shortages unveiled vulnerabilities in supply chains that were previously unseen. The drastic change of the landscape forced supply chain executives to up their strategic management game. To do so, some of them have turned to innovative technologies, with supply chain AI leading the race. In fact, while it is a standard practice for enterprises to hold up their digital transformation projects in times of economic uncertainty, the COVID-19 crisis did not stop supply chain decision-makers from turning to artificial intelligence solutions providers.

SINGAPORE, February 3rd, 2022 – Businesses and public sector organizations will need to accelerate the modernization of their IT infrastructure and operations to be able to build a sustainable competitive advantage in the next 2 to 3 years. The ability to align to the digital paradigm is not only contingent upon investing in next-generation cloud-native IT infrastructure technologies, platforms, and solutions, but also how CIOs will help transform to autonomous IT operations using AI / ML technologies, delivering business resilience, agility, flexibility, and adaptability. The rapid proliferation of data-driven edge workloads, growing number of ransomware and malware attacks, and blistering growth in the volume of structured and unstructured data are creating significant challenges, as a result of which by 2023, most C-Suite will implement business-critical KPIs tied to data availability, recovery, and stewardship. IDC believes this will help to sustain data-driven innovation. "The CIO and IT decision-makers will need to do some serious thinking beyond modernizing the technology building blocks and platforms if they truly intend to align to digital business outcomes, SLAs, and KPIs. Cultural and mindset change is going to be one of the keystones of digital infrastructure paradigm, which goes far and beyond just embracing cloud as the defacto delivery platform or using OPEX based as-a-service IT consumption models. Digital Infrastructure represents the dawn of a new era for IT decision-makers to make an inedible mark in helping their organization lead into the future," says Rajnish Arora, Vice President Enterprise Infrastructure Research at IDC Asia/Pacific.

A recent Accenture research on the state of machine learning (ML) in enterprises indicates 84% of C-suite executives believe they must leverage artificial intelligence (AI) to achieve their growth objectives, yet 76% report they struggle with how to scale. The key challenges in scaling ML in enterprises are availability of quality data and ability to explain ML outcomes; here we will discuss how Master Data Management (MDM) can help address these challenges. In 1959, Arthur Samuel defined machine learning as "... gives computers the ability to learn without being explicitly programmed". And how exactly is that achieved? At its core, machine learning is the application of statistical methods to uncover patterns in the training data and make predictions or decisions without being explicitly programmed.

Artificial intelligence can enhance efficiency and productivity in financial services and is hence emerging as an important tool in the industry. It can reduce human errors and biases, along with improving the quality by spotting anomalies that cannot be picked up from current reporting methods. We caught up with Jayashree Mitra, the head of technology (end-user services), Asia Pacific, to understand more about AI and automation in this industry. She has 23 years of cross-industry experience, of which she has spent 20 years working in Financial Services Technology for Standard Chartered Bank and UBS. Jayashree Mitra: Throughout my childhood, I was always taught the virtues of self-reliance.