The start of a new year always brings renewed hope and expectation. But this time its amplified by the fact that we are embarking on a new decade .. giving us time enough to fulfill our goals, dreams and aspirations.
I am looking forward to many things this year:
- a new job (and possibly, career)
- a new book (about the role of AI and its social impact)
- a new podcast series (mirroring topics from the forthcoming book)
- new challenges and adventures both for me, my wife and children (my eldest sons are at key stages of a University degree and GCSEs and my youngest turns 6 in a few days and is literally counting the days!)
Whatever happens, I sincerely hope that I am able to make a new start and turn this decade into a one that brings prosperity, peace and hope both for my immediate family and my community of friends and loved ones located across the world.
Wish everyone I am connected to both in the physical, virtual and spiritual world every blessing.
AI and Robots
I am excited about becoming a member & contributor to Hanson Robotics – makers of the social humanoid robot Sophia.
Even more exciting is getting access to ‘Little Sophia’.
The interaction between Little Sophia and users focuses on storytelling and learning new things. She’s not just another robot toy built by a toy company, Little Sophia has been designed and built by the same renowned scientists, roboticists and engineers who built Sophia the Robot.
Little Sophia has the same endearing personality as Sophia the Robot. She is intensely curious, refreshingly innocent, and uniquely playful. She is the only consumer robot with a human-like face who can generate a wide range of human facial expressions. She not only responds to commands, but also actively engages in conversations. This unparalleled responsiveness together with her humanoid design makes Little Sophia a smart, educational companion.
About Hanson Robotics
Frequently Asked Questions
The rise of Product Management and scalable Agile delivery practices (e.g. SAFe, DAD, …) have led to diluting (or even deferring) “strategic planning” and “design thinking” enabled by architecture. Instead, architecture has been reduced to a project level support role to get “tech teams” to help launch new products.
The opportunity for Enterprise and Business Architecture is to stitch building blocks and siloed solutions together to form a tapestry that reflects the goal, vision and mission of the over-arching business.
Companies often forget that while technology may be crucial to their success, they are in fact not built or intended to work like modern day software technology companies such as Facebook, Amazon, etc. but rather businesses that serve to support our society and economy via Manufacturing, Aerospace, Retail, Healthcare and Pharma (to name a few).
We need to refresh the conversation about the role, purpose and value of Enterprise AND Business Architecture if we are to have a chance to genuinely be competitive and innovative over the long term rather than reactively adopt emerging technology without a sound game plan.
Click the link to read the advertised article by Anthony Hill that explores this topic further.
‘Conversational AI’ refers to the use of messaging apps, speech-based assistants and chatbots to automate communication and simulate conversations to create personalized customer experiences at scale.
Both the terms ‘Chatbot’ and ‘Conversational AI’ have the same meaning. ‘Conversational AI’, however, is more inclusive of all the technology that falls under the bot umbrella like voice bots and voice + text assistants, whereas ‘chatbots’ have a more limited ‘text-only’ connotation.
Businesses can use ‘Conversational AI’ to automate customer-facing touchpoints everywhere – on social media platforms like Facebook & Twitter, on their website, their app or even on voice assistants like Alexa & Google Home.
NVIDIA’s ‘Conversational AI’ becomes the first platform to train one of the most advanced AI language models – BERT, within an hour and complete AI inference in just over 2 milliseconds. This trailblazing level of performance makes it possible for developers to leverage state-of-the-art language understanding for large-scale applications they can make accessible to hundreds of millions of consumers globally.
The world of AI has just taken a major step forward… thanks to #NVIDIA !
Click on link to read the NVIDIA article for more details.
#ai #emergingtech #conversationalai #dataops #analytics #voicebots #assistants #bert
Do you still carry cash in your wallet? Do you actually have a wallet? Or have you transitioned to e-wallets and digital currency?
The future of money and the analysis of the development of the cashless society which includes the analysis of the means to achieve it and the analysis of the challenges and benefits it can bring, have been studied and discussed by academia on a regular basis in Europe since the early 2010s.
New payment solutions as disruptive technologies, emerging payment technologies, emerging payment business models, biometric payments, integrity, and privacy, and the design of new payments and technologies continue emerging.
Sweden goes from being the first in adopting banknotes in Europe in 1661 to introducing its own digital currency in 2021, and becoming the first world’s cashless society in 2023.
India could be a ‘cashless model’ for the world .. Growing smartphone use and crashing data costs have helped cashless economy to grow immensely.
Cashless society is happening. There’s no turning back !
Click link to read more in this BCG post.
#Cashless #DigitalTransformation #automation #contactless #epayments
Success of AI / ML adoption requires alignment of multidisciplinary teams, collaboration practices (such as DevOps + DataOps) and access to large volumes of (relatively) clean (curated) data.
AI requires selecting platforms, tools, and infrastructure that needs to be ramped up and down as experiments are conducted. Teams can consider a multitude of platforms (TensorFlow, Keras, PyTorch, Caffe), cloud provider (AWS, Azure, Bluemix, Google), and a growing number of collaboration platforms (Dataiku, H20.ai, Databricks, Anodot, Clusterone and others) as part of their AI and machine learning environment. The job of architecture and product management teams.
With data ready, teams need a working process for running experiments using Agile practices and governance mechanisms to capture metadata and results – for analysis and review – to determine success levels and what follow-on experiments to prioritise. This involves a level of strategic thinking and portfolio planning.
When results yield satisfactory results, teams need to determine how to establish a production process to run new data through the AI models.
The article references tangible success stories.
#dataops #devops #enterprisearchitecture #productmanagement #emergingtech #analytics #AI #ML #Agile
Businesses today need to quickly deliver data to support more analytic applications, Data Science and AI, and microservices, used by their line of business personnel as well as their customers.
Modern Data Architectures need to be developed to accommodate these new analytic requirements, larger data volumes and take advantage of newer technologies such as cloud, data lakes and data streaming.
Additionally, the concurrency of the data is becoming imperative, e.g. compliance & regulatory data, web data, social media, etc.
Putting DataOps in place along with procedures for sharing and using data across enterprise (by way of Business & Enterprise Architecture and IT Strategy & Planning) will enable every business user to deliver and provision data with ease and speed – to provide the competitive edge many companies seek.
But what is DataOps?
DataOps is a collaborative Data Management practice introduced in an official manner in mid-2018 on the Gartner 2018 Hype Cycle. It is not a technology solution, but a process for managing data, people and technology in a manner that improves efficiency and ways in which data is used across a company.
DataOps is the application of DevOps practices to data management and integration combined with AL/ML to reduce the cycle time of data analytics, with a focus on automation, collaboration and monitoring.
It offers a blueprint to solve architectural complexities and data challenges posed by data drift, hybrid multi-cloud architectures and real-time analytics (+ AI and ML).
Key Concerns & Points for Reflection
– What is DataOps?
– How it can help Data Scientist send more time modeling and less time working with data.
– It is important to have a view of the entire lifecycle of the data from source through the pipelines to operation.
– Some companies are automating data access to provide self-service to analysts.
– Data sources are growing (GSK has 1,800 sources), data is constantly changing, requiring rework or faulty results.
– The concurrency of the data is becoming imperative, e.g. wellhead data, web data, social media, etc.
– Putting DataOps in place can be the competitive edge many companies seek.
– The technology and product space is rapidly changing – which means everyone (especially IT Leaders, CTOs, CIOs, Business & Enterprise Architects) needs to keep up by getting their ‘hands dirty’ so that they can create scalable architectures and business centric solutions that provide real tangible benefits and ROI.
The key point to remember is that DataOps is a process – not a solution that can be purchased and installed on the network or in the cloud – and a core component of (future) enterprise Data Architecture.