AutoStore Innovates with Pay-Per-Use
An important development in the automated storage and retrieval system industry from one of the world’s largest and oldest manufacturers. This should accelerate the development of micro-distribution centers.
An important development in the automated storage and retrieval system industry from one of the world’s largest and oldest manufacturers. This should accelerate the development of micro-distribution centers.
This week’s The Economist reports on the challenges facing the use of AI in intelligence agencies. One of the issues cited in the article, “Spy Agencies Have High Hopes for AI”, is the dearth of adequate sets of information that can be ingested. AI depends on massive sets of data to be effective and in a number of intelligence settings there is limited available data.
Fast Company published two articles in its March / April 2021 issue on the subject of AI ethics, “AI Has a Big Tech Problem”, and “AI Ethics: Taking Stock and the Way Forward”. (Sorry the online links aren’t available – please contact us if you’d like copies of these articles) The overriding message in these articles is that the thought leadership in AI is concentrated in a limited number of firms and institutions. The risk reported is that ethical decisions made by this small cohort may be influenced, however subtly, by other demands – and not necessarily in the best interest of society as a whole. Something to consider.
A fascinating AI development in the world of debates that leverages crowdsourcing, natural language processing (NLP), natural language understanding (NLU), sentiment analysis, idiom understanding and language generation. See Project Debater.
Here are the results from the first debate hosted by Bloomberg and IBM that featured former U.S. Secretary of Labor Robert Reich and former U.S. Treasury Secretary Lawrence H. Summers, former Greek Finance Minister Yanis Varoufakis and Manhattan Institute Senior Fellow Allison Schrager.
One facet of digital transformation that is currently under intense discussion is the utility of citizen development technologies such as low-code, no-code, RPA, etc. The argument against using these technologies is that these tools and products lack the industrial strength of other technologies. The best way to automate is not to use these easy-to-use technologies or at best use them in a transitional fashion. We find this argument reminiscent of the early days when advocates of low level languages, like assembler, dismissed the high level languages, like COBOL, as inefficient and not suited for high volume work. All of us from that era knew how that worked out – improved compute performance and the ease-of-use of high level languages led to the first digital transformation across industries. The focus of the current arguments are misplaced. Firms should employ technologies that are fit for purpose. Citizen developed solutions may be the perfect solutions in a variety of situations. The important decision has always been to determine the needs and map those needs against the near-term and long-term use of any technology. If it works today, but does not scale – and scale is a requirement, that is a problem. If the solution adds unacceptable complexity or severe maintenance risks, that is also a problem. If the technology will not deliver results to market-based timelines, you may not be using the correct technology solution. If the technology does not have the required flexibility, that is a problem. This all boils down to making informed decisions when deploying technology. There’s nothing new here. It’s also important to note that in some cases not using technology at all is the right course of action!
A great report on the state of Intelligent Automation by Barbara Hodge of SSON. The report includes some very useful information no matter where you are on your Intelligent Automation journey.
Kudos to Microsoft for launching a program to provide education and other resources to help individuals develop skills for the digital economy. As can be seen in the chart, the demand for digital talent is and will be huge. As part of the Microsoft program, training will be offered for free and certifications will be offered at dramatically reduced prices.
A fascinating article on NLP was recently published in Patterns. The article describes the use of NLP to perform sentiment analysis of 4,313 scientific abstracts to determine the successes and failures of species reintroduction. For comparison, a similar manual research effort against 361 published articles “required months of effort from highly trained experts just to obtain the raw data, which they then had to analyze.”
This is another example of how AI can achieve remarkable results. The good news is that the AI analysis shows improvements in the endangered species populations!
A provocative, yet upbeat look at technology’s impact on society. Despite all of the challenges with advances in technology, the best is yet to come.
An interesting article in Fast Company on the opportunities and challenges facing the use of machine learning in healthcare.
A great article in The Economist on the potential of machine learning and the importance of “clean” inputs or signals.