Once you know your team’s current skillsets, you are ready to identify the gaps between the skills you have and the skills you need. This requires peering into your crystal ball to anticipate the skills you will need to be future-ready. You can do this by benchmarking your team’s skills against the skills hired by your most innovative peers, gathering data on the skills that are growing fastest in your industry, and assessing the capabilities you need to meet your business goals. ... The last step is to determine whether it is more efficient to buy or build the skills your team needs by comparing the effort each of these will take. To assess the effort to buy a skill, consider how prevalent it is in the market and whether it is likely to come with a salary premium. Is it a common skill that many workers have, or is it still rare? Will requesting that skill drive up the salary you must pay? The lower a skill’s supply and the higher its salary premium, the greater your training ROI is likely to be if you can build that skill internally. Even if the effort to build a skill is prohibitive, you may still need to buy it, even if it is hard or expensive to find.
Burnout threatens cybersecurity in multiple ways. First, on the employee side. "Human error is one of the biggest causes of data breaches in organisations, and the risk of causing a data breach or falling for a phishing attack is only heightened when employees are stressed and burned out," says Josh Yavor, chief information security officer (CISO) at enterprise security solutions provider Tessian. A study conducted by Tessian and Stanford University in 2020 found that 88% of data breach incidents were caused by human error. Nearly half (47%) cited distraction as the top reason for falling for a phishing scam, while 44% blamed tiredness or stress. "Why? Because when people are stressed or burned out, their cognitive load is overwhelmed and this makes spotting the signs of a phishing attack so much more difficult," Yavor tells ZDNet. Threat actors are wise to this fact, too: "Not only are they making spear-phishing campaigns more sophisticated, but they are targeting recipients during the afternoon slump, when people are most likely to be tired or distracted. Our data showed that most phishing attacks are sent between 2pm and 6pm."
The importance of master data management is obvious: users can only make the correct decisions if the data they use is consistent and correct. MDM ensures consistency and quality on cross-domain level. Organizations need to find a balance. Introducing too many areas of master data or reference values will introduce too much cross-domain alignment. No enterprise data at all makes it impossible to compare any results. A practical way to begin implementing MDM into your organization is to start with the simplest way of master data management: implementing a repository. With a repository you can quickly deliver value by learning what data needs to be aligned or is of bad quality without adjusting any of your domain systems. A next step will be setting clearer scope. Don’t fall into the trap of enterprise data unification by selecting all data. Start with subjects that add most value, such as customers, contracts, organizational units, or products. Only select the most important fields to master. The number of attributes should be in the tens, not the hundreds.
Data warehouses popularized immutability because it eased scalability, especially in a distributed system. Analytical queries could be accelerated by caching heavily-accessed read-only data in RAM or SSDs. If the cached data was mutable and potentially changing, it would have to be continuously checked against the original source to avoid becoming stale or erroneous. This would have added to the operational complexity of the data warehouse; immutable data, on the other hand, created no such headaches. Immutability also reduces the risk of accidental data deletion, a significant benefit in certain use cases. Take health care and patient health records. Something like a new medical prescription would be added rather than written over existing or expired prescriptions so that you always have a complete medical history. More recently, companies tried to pair stream publishing systems such as Kafka and Kinesis with immutable data warehouses for analytics. The event systems captured IoT and web events and stored them as log files.
While Meta’s Big Tech competitors — Amazon, Apple, and Google — already have popular voice assistant products, either on mobile or as standalone hardware like Alexa, Meta doesn’t. “When we have glasses on our faces, that will be the first time an AI system will be able to really see the world from our perspective — see what we see, hear what we hear, and more,” said Zuckerberg. “So the ability and expectation we have for AI systems will be much higher.” To meet those expectations, the company says it’s been developing a project called CAIRaoke, a self-learning AI neural model (that’s a statistical model based on biological networks in the human brain) to power its voice assistant. This model uses “self-supervised learning,” meaning that rather than being trained on large datasets the way many other AI models are, the AI can essentially teach itself. “Before, all the blocks were built separately, and then you sort of glued them together,” Meta’s managing director of AI research, Joëlle Pineau, told Recode. “As we move to self-supervised learning, we have the ability to learn the whole conversation.”
Paul Ducklin, principal research scientist for Sophos, called out Samsung coders for committing “a cardinal cryptographic sin.” Namely, “They used a proper encryption algorithm (in this case, AES-GCM) improperly,” he explained to Threatpost via email on Thursday. “Loosely speaking, AES-GCM needs a fresh burst of securely chosen random data for every new encryption operation – that’s not just a ‘nice-to-have’ feature, it’s an algorithmic requirement. In internet standards language, it’s a MUST, not a SHOULD,” Ducklin emphasized. “That fresh-every-time randomness (12 bytes’ worth at least for the AES-GCM cipher mode) is known as a ‘nonce,’ short for Number Used Once – a jargon word that cryptographic programmers should treat as an *command*, not merely as a noun.” Unfortunately, Samsung’s supposedly secure cryptographic code didn’t enforce that requirement, Ducklin explained. “Indeed, it allowed an app running outside the secure encryption hardware component not only to influence the nonces used inside it, but even to choose those nonces exactly, deliberately and malevolently, repeating them as often as the app’s creator wanted.”
The use cases for GPT-3 in financial services are broad and already encompassed in specific machine-learning packages. For instance, sentiment analysis (using social media and articles to capture the temperature of the market), entity recognition (classification of documents), and translation are all widely available and used. Where GPT-3 will likely come into play for banks is in language generation – the ability to handle claims and fill information into forms, for example. This might be a small, consumer-focused start, but with enough training data, GPT-3 could start taking an active role in risk management and investment decisions. Getting a handle on the current return on RoI for this tech in banking is difficult. These ML elements exist, but as data volumes grow, the need for massive industry and even bank-specific trained models is clearer. One big problem for financial institutions able to access the model (OpenAI is less closed these days but GPT-3 is limited in terms of pre-training, downstream task fine-tuning, plus no industry-specific corpus) is finding the people to make it all work.
Given an accident between two vehicles, multiple pieces of information can be easily shared and/or recorded for financial transactions including insurance coverage, the costs of subsequent repairs or medical bills, the percent culpability by both parties, etc. Over time, this creates a digital history of a vehicle, which can be used to avoid deceit. “Fraud is big expense for insurance carriers,” explains Shivani Govil, Chief Product Officer at CCC Intelligent Solutions, “In the U.S. alone, fraud costs the insurance industry over $40 billion annually. ... In the coming years, manufacturers, governments, repair shops and suppliers will need to track cybersecurity certifications based upon software versions of every part, especially since soon some countries will requires proof of cybersecurity management systems and software update capability. The vast Bill of Materials for a given vehicle might include hundreds of parts with different versions software which may have been replaced last week after an accident. Does the replacement part have updated code?
This move towards new forms of interaction is a trend that resonates with Mia Sorgi, director of digital product and experience at food and drink giant PepsiCo Europe, whose company ran a gesture-based project recently that allowed customers in a KFC restaurant to be served by moving their hands, with no contact required. "I'm really proud of the work we did here," she says. "I believe that gesture is a very important emerging interface option. I think it is something that we will be doing more of in the future. I think it's really valuable to get an understanding of how to win in that space, and how to create something successful that people can use." While PepsiCo's gesture-based project received fresh impetus during the pandemic, Sorgi explains to ZDNet that the company has been experimenting with touchless technology for the past three years. Those initial investigations into gesture were scaled up and explored in a business environment last year.
Technology has undoubtably become influential in all aspects of our daily lives and has hit every part of the retail ecosystem. The impacts of the pandemic created a boom of online shoppers, who aren’t going away any time soon. Consumers are now more inclined to search and buy products online, with unlimited options as the tantalising grip – retailers though must endeavour to continuously adapt to keep ever with ever evolving customer demands. This goes for physical stores too, as customers that become more tech savvy increasingly expect brick-and-mortar stores to keep up with exciting and new digital innovations. An additional part of this retail ecosystem that can be revolutionised by technology is the industry’s operations, which can and should be stabilised and made more efficient. Whilst no retailer can predict what will happen in the future, they can invest in technology that helps cope with erratic seasonal or supply rise and falls and help prepare for whatever lies ahead. Investing in software solutions that can help to stabilise operations and prepare for unknown terrains is a good place to start.
Quote for the day:
"Confident and courageous leaders have no problems pointing out their own weaknesses and ignorance." -- Thom S. Rainer