Daniel Connell oversees the Market Structure and Technology practice at Greenwich Associates. Prior to Greenwich Associates, he was the CEO at Correlix, Inc., a leading provider of real-time performance optimization technology. Formerly, Dan was the Executive Managing Director at Standard & Poor's, and CEO of ComStock, Inc. He also served as the COO of Xinhua Finance Ltd., China's premier financial information, and Media Company. He has also been an executive-in-residence in the private equity industry and as a Board Member and strategy consultant to technology-focused content companies.
As we progress further into Industry 4.0, finance needs to further leverage new technologies to add real value to a business's bottom-line, yet it remains in its infancy stages. Industry 4.0 has impacted a range of industries, and with the digitisation of industrial value chains, many forget about finance, which has only touched the tip of the iceberg when it comes to leveraging new technologies. Disruptive technologies such automation, artificial intelligence (AI), the Internet of Things (IoT), Bots, blockchain and machine learning are thrusting the global economy into a new digital era. Instead of seeing all these new developments in isolation, finance must focus rather on connection points, finding ways to optimise them to provide greater value to the organisation. Companies risk losing ground if they do not understand the changes and opportunities Industry 4.0 brings.
Instead, engineers will need to design systems that offer scalable performance, that are able to dynamically adjust the type of processing resource they deliver based on the task at hand. This is different to what embedded engineers may be comfortable with right now. For some years embedded processors have had the ability to vary their operating frequency and supply voltage based on workload. Essentially, a processor's core can run slower when it isn't busy; scaling back the main clock frequency directly translates to fewer transistors switching on and off per second, which saves power. When the core really needs to get busy, the clock frequency is scaled up, increasing the throughput.
Making data quickly actionable creates difficult challenges for the old data management order. Three new reports from Gartner bring into sharp focus the increasing urgency for enterprises of building value-generating operational applications infused with AI and ML – or risk falling forever behind. Urgency Builder #1: In its latest AI business value forecast, Gartner says that AI augmentation will create $2.9 trillion of business value in 2021. Urgency Builder #2: Gartner's AI and ML Development Strategy study finds that leading organizations expect to massively increase their AI/ML projects – from a mean of four this year to 35 by 2022. Urgency Builder #3: In its "Predicts 2019: Data & Analytics Strategy" report, Gartner says, "Effective data management is more critical than ever. While some companies have taken control of their data and turned it into a weapon for securing market dominance, many others are struggling with an issue that is putting the brakes on intelligence coordination: silos."
Arthur C. Clarke's famous comment, "Any sufficiently advanced technology is indistinguishable from magic," seems especially apt today, with cars driving themselves and phones starting to do real-time language translation. But the real super-power behind our smartphones, apps, servers, automated homes and autonomous vehicles isn't magic – it's silicon, in the form of advanced processor and memory chips. A vivid reminder of this was provided by Samsung Semiconductor, a world leader in advanced semiconductor technology, at its Samsung Tech Day on Oct. 23 in San Jose. The annual event is part of Samsung's ongoing effort to foster innovation across the technology ecosystem, with hundreds of attendees learning about and discussing the future of consumer and business technology sectors. Chip-stacking is a perfect example of how behind-the-scenes innovation creates new possibilities, like the 12GB LPDDR4X uMCP (UFS-based multichip package).
The organisation of tomorrow will be built around data using emerging technologies. Big data analytics empowers consumers and employees. This will result in real-time decision making and a better understanding of the changing environment. Blockchain enables peer-to-peer collaboration and trustless interactions governed by cryptography and smart contracts. Meanwhile, artificial intelligence allows for new and different levels of intensity and involvement among human and artificial actors.
Artificial intelligence is arguably the most disruptive technology to emerge over the last few decades. Consumers are producing data at record levels. It's estimated we'll produce 463 exabytes per day by 2025. Yet humans aren't equipped to process that complex information. We're starting to rely more on AI to interpret massive amounts of consumer and third-party data in real time, and to make it relevant for our uses.
Today, businesses rely heavily on data for insight. At the same time, there are many data-driven models and solutions available for businesses to choose from to get their insight. Be it for understanding customer behaviour or getting real-time information from the ground, data-driven models require the right tools to ensure efficiency. With artificial intelligence being an enabler for automation to get faster results, businesses need a solid foundation in their business model. AI business models can be used on almost any type of business today.
The'why' of the DepthAI (that satisfyingly rhymes) is we're actually shooting for a final product which we hope will save the lives of people who ride bikes, and help to make bike commuting possible again for many. What we envisioned is a technology-equivalent of a person riding backwards on your bike holding a fog horn and an ambulance-LED strip, who would tap you on the shoulder when they noticed a distracted driver, and would use the LED strip and the horn judiciously to get the attention of distracted drivers - to get them to swerve out of the way. In working towards solving this problem, we discovered there was no solution on the market for the real-time situational awareness needed to accomplish this. So we decided to make it. In doing that, we realized how useful such an embeddable device would be across so many industries, and decided to build it as a platform not only for ourselves, but also for anyone else who could benefit from this real-time object localization (what objects are, and where they are in the physical world).
In MEAP, you read a book chapter-by-chapter while it's being written and get the final book as soon as it's finished. Save big on Manning books and liveVideo courses with our exclusive bundles! Each bundle is carefully curated to enhance your skills in a key subject area. Deep learning is exploding, driving everything from autonomous vehicles to real-time computer vision and speech recognition. New languages and new approaches to programming are always emerging.