Enterprise big data and analytics cuts through the hype to make sense of data collection, storage, management, dissemination and discovery technologies, all employed collectively as a means of realizing corporate efficiencies and uncovering business opportunities.
Enterprises should be prepared to be ‘guinea pigs’ for large tech companies seeking to develop replicable AI solutions.
Off-the-shelf AI solutions for vertical and horizontal use cases are being offered by a growing number of providers.
One of the biggest challenges to adopting AI is knowing where to start. In theory, AI can be applied to any and all aspects of an organization’s day-to-day operations. Furthermore, even if AI enhances a particular part of a business’s operations, it does not necessarily mean that the value returned will be worth the investment. One of the biggest beasts in the telecoms technology world, Cisco, has acknowledged that it has not brought as many AI-enhanced solutions to the market as it anticipated because it is still developing the use cases for AI. Continue reading “Making Money from AI: Use Cases and Experimentation”→
Starting on September 1, 2019, Microsoft will begin onboarding new Office 365 users directly into Microsoft Teams, in essence removing the option for customers to run both Teams and the soon-to-be-retired Skype for Business Online.
Though somewhat extreme, this migration plan has been coming on for some time now, frankly ever since Microsoft introduced Microsoft Teams in 2017.
• Whilst AI can replace humans, it often works best when used to enhance what humans are doing.
• AI can deliver significant business benefits, but if implemented unsympathetically it can also cause disruption.
GlobalData’s research indicates that businesses understand that AI offers significant potential benefits in areas such as efficiency, R&D, and staff training, recruitment, and retention. The same research finds that enterprises also see potential pitfalls. Whilst the 5% of respondents in GlobalData’s survey who stated that AI is the ‘beginning of the end of the world’ may have had their tongues in their cheeks, a level of concern is not uncommon. Indeed, KPMG has referred to the concept of ‘Robocalypse Now’. It is also not unreasonable for employees to be worried that AI driven automation technologies will mean job losses because the adoption of those solutions usually does lead to headcount reductions. Continue reading “Humanizing AI: How to Automate in a Sympathetic Way”→
• Late last week AT&T and Samsung together cut the ribbon on a co-developed 5G Innovation Zone that had nothing at all to do with consumer 5G future opportunities.
• Rather, the new facility, housed within Samsung Austin Semiconductor’s Austin Texas fabrication plant, showcased several ways high speed cellular can both modernize and optimize manufacturing processes.
If you travel a few miles northeast of Austin, Texas, you’ll find among the gentle rolling hills an undistinguished 300-acre facility dedicated to the fabrication of semiconductors (aka computer chips) for networking, high performance computing, IoT, and of course mobile devices. And if you look carefully within the foyer of this 20+ year old foundry, you’ll find a somewhat unassuming highly rectangular room peppered with Ikea-styled demonstration tables and plain black monitors that when considered together scream out in all caps: “5G IS VERY REAL, RIGHT NOW!”
Consumers are becoming aware that their personal data is being mined and misused. They will demand changes and control.
Companies, starting with IT departments, need to get in front of this trend and become more customer-conscious about personal data and privacy by giving customers control and choice about how their data is used before laws and regulations make it no choice at all.
The definition of ‘me’ is expanding. ‘Me’ used to be about personal identity and one’s physical person, perhaps even extending to the immediate family around you. ‘Me’ is getting bigger, though, and extends to a lot more things. ‘Me’ is now also anything about ‘me’ including metadata about me. ‘Me’ is the data I generate from just living, the things I do, the products I buy, the music I like to listen to, and the entertainment I enjoy. ‘Me’ is browsing habits, daily habits, the places I go, the things I stop and look at in stores; my preferences for temperature, color, and foods; even my face, my eyes, my fingerprints, the patterns of veins in my hands. Continue reading “It’s All About ‘Me’”→
Newly published research shows language in Facebook posts can be a more accurate tool than demographic data for helping medical professionals make a diagnosis.
The Facebook data is particularly effective in shedding light on certain health issues including diabetes and mental illness.
Facebook has been under fire for years for everything from the Cambridge Analytica scandal to the platform’s part in aiding the dissemination of false information about the Rohingya Muslims that led to the deaths of thousands in Myanmar. Though it is sometimes derided as a tool that does more to isolate than connect, newly published findings by researchers from Penn Medicine and Stony Brook University show Facebook posts can provide important clues to puzzle out a number of medical conditions including diabetes, depression, and psychosis. Continue reading “Research Finds Facebook Posts Could Help Doctors Diagnose Medical Conditions”→
• There has been a significant rush among technology providers to make artificial intelligence (AI) a self-service endeavor, to make it available to the broadest possible swath of business users.
• But in so doing, companies are creating unanticipated legal exposure for AI practitioners unprepared to protect AI from human bias.
Salesforce.com has added a new AI learning module to its Trailhead developer education platform with an interesting twist. Rather than teach developers how to build AI outcomes most efficiently, the company’s newest educational module asks that practitioners slow down and focus on creating ethically informed AI solutions.
The new Trailhead educational module entitled, “Responsible Creation of Artificial Intelligence,” calls attention to an often overlooked threat from AI, namely unwitting human biases and intentional human prejudices.
Within these new training materials, Salesforce.com calls on Salesforce.com Einstein developers to adopt its own set of core values of “trust, customer success, innovation, and equality.” The company goes so far as to suggest that developers who fail to adhere to these standards in creating AI algorithms may find themselves in breach of its acceptable use policy.
Why is Salesforce.com referencing an acceptable use policy in conjunction with the ethical use of AI? Surely companies not engaged in outright nefarious endeavors would steer clear of anything overtly illegal in building AI outcomes. Certainly legislative controls such as GDPR and the California Consumer Privacy Act (CCPA) are very clear about what constitutes an unlawful use of consumer data. Companies need only adhere to such policies to avoid potential litigation or censure, right?
• Cost sharing between vendors/SPs and customers can strengthen relationships in a difficult time.
• Calm and deliberate planning by vendors/SPs and customers is key to minimizing impacts to business.
The new tariffs on imported goods in China and the U.S. will have a significant impact on pending and future deals, both for service providers, vendors, and customers. The technology industry has a complex and deeply international supply chain, with U.S. and Chinese companies both utilizing components and intellectual property. Component price increases will lead to sharp increases in product costs. These increases will slow or stall deals as customers may wait and see if the issues can be resolved in a short time frame. Continue reading “Geopolitical Issues Roil IT Sector”→
• Many organizations need help navigating ethical issues related to artificial intelligence (AI), such as privacy laws, unintentional bias, and lack of model transparency, but don’t know where to begin.
• Enterprises can benefit from working with a partner that helps them consider the ethical implications of their AI deployments, but they should keep in mind that issues aren’t static and can evolve over time.
Organizations are eager to enjoy the benefits that AI can bring to them – whether enhanced productivity, or new revenue-generating or enhanced customer experience opportunities. But many are unclear about how to navigate the murky waters of AI and ethics. Changing regulations and privacy laws, concerns over unintentional bias in training data, lack of transparency in AI models, and the dearth of experience with new use cases are difficult challenges to address. Enterprises want to ensure that their adoption of the technology doesn’t cross ethical boundaries, but often don’t know where to begin. Thankfully, the topic is being increasingly addressed by IT services providers. Many organizations, from IBM to Capgemini to Atos are touting that they help their customers implement AI while also considering the ethical implications of their deployment. Continue reading “AI and Ethics: The Waters are Murky, but Help is Available”→
• In order to do AI, IoT, and other big data-dependent projects right, companies are beyond the confines of traditional relational databases.
• Two recent, related partnerships between highly specialized “graph” database developers, Neo4J and TigerGraph, and public cloud platform providers, Amazon and Google, underscores the importance surfacing insights that would otherwise remain hidden within traditional database architectures.
Organizations anxious to put AI to work as a means of driving innovation must first invest in big data. AI algorithms and predictive models are nothing without a constant influx of high quality data. The trouble is that not all data is created equal, at least in terms of its ability to match the demands of a given initiative, be that AI, IoT, mobility, or edge computing.
Such specific demands in turn drive the adoption of highly specialized data architecture, extending down to the database itself. There are traditional relational databases as well as those specializing in key-values, document storage, in-memory processing, time-series evaluation, transaction ledgers, and graph analysis. Each in turn solves very specific problems – e.g., self-driving cars won’t work without an underlying database capable of performing time-series analysis. Continue reading “Graph DB Makers Neo4J and TigerGraph Explore Bring Your Own Database Cloud Options”→