Right now, the broadcast media is obsessing with the risks of Artificial Intelligence. Quoting the late physics genus Stephen Hawking.“The development of artificial intelligence could spell the end of the human race,” , with concern now exacerbated with knowledge of the personalities divining advances in the technology. According to recent debate on Talk Radio, even some of those briefing against AI are part of a global conspiracy to restrict access to AI development to a chosen few. “AI scares the hell out of me, says Elon Musk once. “It’s capable of vastly more than almost anyone knows, and the rate of improvement is exponential.” But before AI underpins the demise of mankind, there are surely some more practical applications in the AV world?
Lasting outcomes of the pandemic include the moves to hybrid and home working, with the corresponding shifts in emphasis to self sufficiency by users of essential work tools and skills. While ‘concierge’ services for users of video-conferencing solutions are now a thing of the past, self-sufficiency among users of such technologies was pretty much thrust upon users with very little time for proper programmes of user adoption. Gergely Vida, CEO of Lightware Visual Engineering Vida has identified a number of areas in which AI can play a positive role including:
Intelligent Automation: AI can automate various aspects of collaborative solutions, making them more user-friendly and efficient. For example, systems powered by AI can automatically set up and configure AV equipment based on user preferences, eliminating the need for manual adjustments. This automation saves time and simplifies the user experience.
Natural Language Processing (NLP): This enables voice recognition and voice control features in collaborative solutions. Users can interact with their AV system using voice commands, making it easier to control and operate devices during meetings. NLP can also facilitate real-time transcription and translation services, enabling seamless communication among participants who speak different languages, or does not understand a common language properly.
Intelligent Content Analysis: AI can analyse the AV content in real time, enabling features like automatic speech and facial recognition, or object detection. These capabilities can enhance collaboration by automatically generating meeting summaries, identifying speakers, tracking audience engagement, or even suggesting relevant content based on the ongoing discussion.
Personalisation and Adaptation: AI can learn and adapt to user preferences and behaviour over time. By analysing user interactions, the algorithms can personalise the experience. For instance, an AI system can learn an individual’s preferred display layout, audio settings, or meeting room configurations, and automatically adjust them.
Intelligent Meeting Assistance: It can act as an intelligent assistant during meetings. AI can provide contextually relevant information, retrieve and display data, schedule follow-up actions, or offer suggestions based on the discussion. These virtual assistants, like chatbots, can answer queries, provide meeting reminders, or even automate routine tasks, allowing participants to focus on the collaboration itself.
The integration of AI technologies can streamline processes, reduce manual efforts, and provide personalised and intelligent features that ultimately lead to more efficient and engaging collaborative experiences. In addition, it could also serve as a marketing tool for tasks such as competitor analysis, market research, spell checking, translating, or copywriting. For example: “We, at Lightware find it beneficial to utilise AI for searching case studies that highlight our solutions and products.” Enhancing the user experience.
Joel Chouinard, CPO at Utelogy believes that AI helps to capitalise on the collective expertise of an organization: “Companies have the autonomy to decide how to amass, consolidate, and visualize data from their collaborative spaces, employing AI to enhance the user experience. By applying machine learning (ML) and artificial intelligence (AI) algorithms to the collected data, we can unearth relationships, predict potential outcomes, aid automated decision-making, and answer pertinent business queries, among other possibilities.”
Chouinard further believes the rush to AI solutions is something of a natural progression: “While predictive modelling and statistical analysis aren’t new, we’ve seen significant advancements in recent years in using machine learning to identify meaningful patterns and insights within the gathered data. This data can be ingested from various sources, including data lakes or data warehouses. Overall, analytics offers critical insights, enabling users or third-party tools to respond, either manually or better yet, through automated processes.”
Software solutions, such as Utelogy, collect and deliver essential operational data from AV/UC systems and rooms to the cloud for analysis, and provide tools for users to enact change via intelligent, customizable, and automated actions. This gathered data can then be processed through AI or ML tools, offering deeper insights into predictive analytics and logical outcomes. “Utelogy uses these actionable analytics to provide clear business insight, optimise workflow, enhance operational health and performance (like self-setup or self-healing), and reduce the need for expensive human intervention. In essence, our solutions are designed to make collaborative solutions more efficient and user-friendly through the power of AI.
The AI skill set:
So what skills and knowledge does an AV integrator need in order to utilise these AI technologies? They require a combination of technical skills and domain expertise. The following are valuable for implementing AI-based solutions:
AI and Machine Learning: A strong understanding of AI and machine learning concepts is essential. This includes knowledge of different machine learning algorithms, deep learning techniques, neural networks, and their applications in computer vision and natural language processing.
Data Science and Analytics: Proficiency in data science and analytics is crucial for working with the vast amount of data generated in the AV domain. This involves skills in data pre-processing, feature engineering, data visualization, and statistical analysis. Knowledge of data mining techniques and experience in working with large datasets will be valuable.
Computer Vision: Given the visual nature of AV solutions, expertise in computer vision is vital. This includes skills in image and video processing, object detection and tracking, image recognition, and understanding spatial relationships in visual data. Experience with libraries like OpenCV and knowledge of convolutional neural networks (CNNs) are relevant.
Software Development: Proficiency in software development is necessary to build AI-based AV solutions. This includes programming skills in languages like Python, Java, or C++, as well as knowledge of software engineering principles, version control, and software testing. Experience with frameworks and tools specific to the AV industry would be advantageous.
Domain Knowledge: A solid understanding of the ProAV market, including its technologies, products, and industry trends, is crucial of course. This knowledge will help in identifying opportunities for AI integration, understanding user requirements, and designing effective solutions tailored to the AV domain.
Technical Support and Communication: Strong collaboration and communication skills are essential for working effectively with multidisciplinary teams in the AV industry. Being able to communicate technical concepts clearly to both technical and non-technical stakeholders is crucial for successful implementation.
The field of AI is rapidly evolving, so staying up to date with the latest research, technologies, and industry developments through continuous learning and professional development is highly beneficial for implementing AI solutions in the AV market. “Lightware recognises the importance of these skills and is committed to developing them further in order to effectively leverage emerging technological advancements in the future,” explains Vida.
AI and digital signage:
AI has the potential to be instrumental in evolving digital signage solutions and networks, particularly in the management of digital signage deployments and operations.
Content Optimization: AI can analyse data and user behaviour to optimise the content displayed on digital signage. By leveraging techniques such as computer vision and data analytics, AI algorithms can identify the demographics, interests, and preferences of the audience in real time. This information can be used to personalise content, select relevant ads, and improve the overall effectiveness of the digital signage.
Audience Analytics: AI-powered analytics can provide valuable insights into the audience’s engagement with digital signage. Computer vision algorithms can track metrics like dwell time, attention span, and audience demographics. This data can help in understanding which content is most effective, identifying patterns, and making data-driven decisions to improve the signage’s impact and ROI (Return on Investment).
Real-Time Updates and Dynamic Content: AI can enable real-time updates and dynamic content. By integrating AI algorithms with data sources such as social media feeds, weather information, or live event data, the signage can display up-to-date and contextually relevant content. This dynamic approach ensures that the displayed information remains fresh, engaging, and useful to the audience.
Automated Monitoring and Maintenance: AI can automate the monitoring and maintenance of these networks. By analysing sensor data and performance metrics, AI algorithms can detect anomalies, predict potential issues, and trigger automated maintenance or repair processes. This proactive approach reduces downtime, improves reliability, and minimizes the need for manual intervention.
Intelligent Scheduling and Targeting: Artificial intelligence has the capability to enhance the optimisation of content scheduling and targeting. By analysing historical data, user behaviour patterns, and contextual information, AI algorithms can determine the most effective times and locations for displaying specific content. This ensures that the right messages reach the right audience at the right time, increasing the impact and relevance of the campaigns.
Predictive Analytics: AI can leverage predictive analytics to anticipate audience behaviour and optimize content strategies accordingly. By analysing historical data and external factors, AI algorithms can generate insights and forecasts about audience preferences, trends, and engagement patterns. This information can be used to plan future campaigns, improve content creation, and drive better outcomes
AI can optimise content, enhance audience engagement, automate maintenance processes, and provide valuable insights for data-driven decision-making. By leveraging AI technologies, businesses can improve the effectiveness of the digital signage strategies. “Lightware considers it important to utilise these new technological advancements, and although we currently do not have a specific solution, we plan to place greater emphasis on this in the future,” promises Vida.
Future potential:
So what of the future? Will a dependence on AI inevitably result in the end of mankind? Well perhaps, but in the meantime, we are likely to see some benefits before the machines take over. For example, AI also holds substantial potential for implementations within the smart building sector, particularly in the realm of building management. This becomes pertinent as we begin to explore the idea of creating tailored user experiences that extend beyond individual buildings, and into our homes, transportation, and communication systems. Companies today are leveraging the power of AI to revolutionize the way they gather, interpret, and present data from their collaborative spaces.
This is especially evident in context of Audio-Visual (AV) and Internet of Things (IoT) technologies, where AI has been instrumental in refining user experiences. The application of machine learning (ML) and artificial intelligence (AI) algorithms has opened up a world of possibilities, from discovering hidden relationships within the data to predicting potential outcomes and facilitating automated decision-making.
Predictive modelling and statistical analysis are further examples of tools which are far from new. The adoption of both, has skyrocketed in recent years. Machine learning is now widely used to identify significant patterns within data collected from diverse sources, including data lakes and warehouses. The insights obtained through analytics are invaluable, enabling users or third-party tools to respond more efficiently, either manually or via automated processes, increasingly powered by AI.
Software tools:
Chouinard says that the software tools essential for AI implementations are pretty much available. In this arena, “Utelogy, a software solution, stands out. Utelogy collects and delivers crucial operational data from AV/UC systems and rooms to the cloud for analysis. Furthermore, it equips users with tools to implement changes through intelligent, customizable and automated actions. These actions can range from smart notifications and task prioritization to automated scheduling and self-healing. The data Utelogy harnesses is available for processing by AI or ML tools, to provide deeper insights into predictive analytics and logical outcomes furthering Utelogy’s tools to proactively take the right action at the right time.”
“Utelogy uses actionable analytics to provide clear business insight, optimize workflows, improve operational health and performance, and reduce the necessity for costly human intervention. For instance, the self-setup or self-healing features allow systems to correct themselves automatically. In essence, Utelogy’s mission is to make collaborative solutions more efficient and user-friendly by harnessing the power of AI.”
AI and associated risks:
AI regulation has become a priority internationally and the suggestion has been made that some AI technologies could be banned. The problem is that there is currently no way of differentiating between ‘good AI’ and ‘bad AI’, with the further problem being that technologies which currently fall in the ‘good’ category might eventually turn out to be ‘bad’. Either way. Politicians and others charged with defining what is ;good’ and what is ‘bad’ are generally considered to have little or understanding or the issues or the technologies..
“The key then is deciding how to apply AI in an ethical manner. On a company level, there are many steps businesses can take when integrating AI into their operations. Organisations can develop processes for monitoring algorithms, compiling high-quality data and explaining the findings of AI algorithms. Leaders could even make AI a part of their company culture, establishing standards to determine acceptable AI technologies.” But what do you do when applying standards to society as a whole?
AI-powered job automation is a pressing concern as the technology is adopted in industries including marketing, manufacturing and healthcare. Eighty-five million jobs are expected to be lost to automation between 2020 and 2025. As AI robots become smarter and more dexterous, the same tasks will require fewer humans. And while it’s true that AI will create 97 million new jobs by 2025, many employees will not have the skills needed for these technical roles and could get left behind if companies do not upskill their workforces.
