The future of artificial intelligence (AI) has been the focus of tech entrepreneurs and financial backers gathered in Doha for the second annual Web Summit hosted by Qatar. The four-day digital technology and emerging innovation summit kicked off its second day on Monday, with attendees eyeing an AI environment being transformed rapidly. Leading entrepreneurs from around the world, including Alexander Wang, founder and CEO of Scale AI, and Alexis Ohanian, co-founder of Reddit and general partner at Seven Seven Six, took centre stage at the event on the opening day. Reporting from Doha, Al Jazeera's Colin Baker said the summit is grappling with questions over the future of AI amid "companies and investors that are changing that landscape more rapidly than we expected". The United States and China are leading in preparedness for AI, said Wang of US company Scale AI.

The rise of AI-assisted software engineering (SE 2.0), powered by Foundation Models (FMs) and FM-powered copilots, has shown promise in improving developer productivity. However, it has also exposed inherent limitations, such as cognitive overload on developers and inefficiencies. We propose a shift towards Software Engineering 3.0 (SE 3.0), an AI-native approach characterized by intent-first, conversation-oriented development between human developers and AI teammates. SE 3.0 envisions AI systems evolving beyond task-driven copilots into intelligent collaborators, capable of deeply understanding and reasoning about software engineering principles and intents. We outline the key components of the SE 3.0 technology stack, which includes Teammate.next for adaptive and personalized AI partnership, IDE.next for intent-first conversation-oriented development, Compiler.next for multi-objective code synthesis, and Runtime.next for SLA-aware execution with edge-computing support. Our vision addresses the inefficiencies and cognitive strain of SE 2.0 by fostering a symbiotic relationship between human developers and AI, maximizing their complementary strengths. We also present a roadmap of challenges that must be overcome to realize our vision of SE 3.0. This paper lays the foundation for future discussions on the role of AI in the next era of software engineering.

I recently had a conversation with a senior executive who had just landed at a new organization. He had been trying to gather new data insights but was frustrated at how long it was taking. After walking his executive team through the data hops, flows, integrations, and processing across different ingestion software, databases, and analytical platforms, they were shocked by the complexity of their current data architecture and technology stack. It was obvious that things had to change for the organization to be able to execute at speed in real time. Data is a key component when it comes to making accurate and timely recommendations and decisions in real time, particularly when organizations try to implement real-time artificial intelligence.

ChatGPT is a generative pretrained transformer language model created using artificial intelligence implemented as chatbot which can provide very detailed responses to a wide variety of questions. As a very contemporary phenomenon, this tool has a wide variety of potential use cases that have yet to be explored. With the significant extent of information on a broad assortment of potential topics, ChatGPT could add value to many information security uses cases both from an efficiency perspective as well as to offer another source of security information that could be used to assist with securing Internet accessible assets of organizations. One information security practice that could benefit from ChatGPT is the reconnaissance phase of penetration testing. This research uses a case study methodology to explore and investigate the uses of ChatGPT in obtaining valuable reconnaissance data. ChatGPT is able to provide many types of intel regarding targeted properties which includes Internet Protocol (IP) address ranges, domain names, network topology, vendor technologies, SSL/TLS ciphers, ports & services, and operating systems used by the target. The reconnaissance information can then be used during the planning phase of a penetration test to determine the tactics, tools, and techniques to guide the later phases of the penetration test in order to discover potential risks such as unpatched software components and security misconfiguration related issues. The study provides insights into how artificial intelligence language models can be used in cybersecurity and contributes to the advancement of penetration testing techniques.

Our client lost only a quarter of the budget they dedicated to an AI project because they chose to start with a proof of concept. The PoC allowed them to test their idea and fail fast with limited spending. To avoid wasting time and effort, always ask your AI solutions consultant for a proof of concept -- especially if your company is just testing the artificial intelligence waters. This article explains what an AI proof of concept is and elaborates on the five steps that will guide you through your first PoC, together with the challenges that you might encounter on the way. It also presents AI PoC examples from our portfolio.

Despite the accelerated adoption of digital in recent years, some organisations are still trying to unlock true value from their transformation efforts to date – however as budgets compress and global economic uncertainty grows, many analysts are suggesting growth in the IT & Digital spend is essential for maintaining competitive advantage and staying operationally efficient. True digital transformation can only be seen when every function of an organisation comes together, providing the essential cohesion to innovate effectively and generate true transformation value. Whatever position a company is in, one thing that is clear is that the current generation of marketing and digital leaders is delivering cutting edge digital transformation programmes means continuing to break down organisational siloes wherever possible. This theme has been emphasised since everyone emerged from the pandemic. James McGough, Founder of Europe's biggest technology EXPO – DTX, believes crucial to this is the need for professionals to learn more about their own organisations: ''We have a lot of key discoveries in this area - 71% of our recent C level visitors have told us they no longer wanted to work in what I call'departmental isolation' and were actively seeking to work with others across their organisation to help realise the digital industry's massive potential." While leading companies can access a new world of possibilities by shifting to digital collaboration in all areas of their company, with technology stacks continuing to change rapidly, many marketing leaders are finding more obstacles to navigate than successes. Alex Vail, Chief Marketing Officer, R2 Factory at Rolls Royce feels more collaboration between all teams in order to share ideas, technology and data in new safe and secure ways is the answer to delivering real change: "Digital Transformation is hard.

The main function of MLOps is to automate the more repeatable steps in the ML workflows of data scientists and ML engineers, from model development and training to model deployment and operation (model serving). Automating these steps creates agility for businesses and better experiences for users and end customers, increasing the speed, power, and reliability of ML. These automated processes can also mitigate risk and free developers from rote tasks, allowing them to spend more time on innovation. This all contributes to the bottom line: a 2021 global study by McKinsey found that companies that successfully scale AI can add as much as 20 percent to their earnings before interest and taxes (EBIT). "It's not uncommon for companies with sophisticated ML capabilities to incubate different ML tools in individual pockets of the business," says Vincent David, senior director for machine learning at Capital One.

Artificial Intelligence (AI) technologies are moving from customized deployments in specific domains towards generic solutions horizontally permeating vertical domains and industries. For instance, decisions on when to perform maintenance of roads or bridges or how to optimize public lighting in view of costs and safety in smart cities are increasingly informed by AI models. While various commercial solutions offer user friendly and easy to use AI as a Service (AIaaS), functionality-wise enabling the democratization of such ecosystems, open-source equivalent ecosystems are lagging behind. In this chapter, we discuss AIaaS functionality and corresponding technology stack and analyze possible realizations using open source user friendly technologies that are suitable for on-premise set-ups of small and medium sized users allowing full control over the data and technological platform without any third-party dependence or vendor lock-in.

Bernd Greifeneder is the CTO and founder of Dynatrace, a software intelligence company that helps to simplify enterprise cloud complexity. Continuous digital transformation now defines modern, competitive organizations. Yet, the infrastructure that supports this transformation--powering everything from mobile banking to personalized, omnichannel retail experiences and "smart" healthcare--is built on complex multicloud architectures. The scale and complexity of these data and application environments are increasing relentlessly, and many companies already use five different cloud service platforms on average, according to research conducted by Coleman Parkes and commissioned by Dynatrace. This complexity exceeds humans' ability to manage.

Any enterprise can unlock AI -- but only if leaders know how to actually leverage their data, define problems and iterate. In this VB On-Demand event, join industry experts as they dig into how enterprises can turn data into company-wide AI and machine learning solutions. Companies are foundering in the quest to realize AI objectives, not to mention a return on their investment in AI, and it comes down to the right data and the right expertise, says Paula Martinez, CEO and co-founder of Marvik, a machine learning consultancy. First, there's the expense and effort of uncovering good quality data from the mountain that's always growing, and labeling it properly to actually put analytics and machine learning developments into production. And then, going from proof of concept to a production-ready solution with quality standards that can be launched at scale is another enormous obstacle -- and a large part of that is finding a team with the right skills to carry out the task successfully.