February 26, 2024

From deepfakes to digital candidates: AI’s political play

Deepfake technology uses AI to create or manipulate still images, video and audio content, making it possible to convincingly swap faces, synthesize speech, fabricate or alter actions in videos. This technology mixes and edits data from real images and videos to produce realistic-looking and-sounding creations that are increasingly difficult to distinguish from authentic content. While there are legitimate educational and entertainment uses for these technologies, they are increasingly being used for less sanguine purposes. Worries abound about the potential of AI-generated deepfakes that impersonate known figures to manipulate public opinion and potentially alter elections. ... Techniques like those used in deepfake technology produce highly realistic and interactive digital representations of fictional or real-life characters. These developments make it technologically possible to simulate conversations with historical figures or create realistic digital personas based on their public records, speeches and writings. One possible new application is that someone (or some group), will put forward an AI-created digital persona for public office.?

How data governance must evolve to meet the generative AI challenge

“With generative AI bringing more data complexity, organizations must have good data governance and privacy policies in place to manage and secure the content used to train these models,” says Kris Lahiri, co-founder and chief security officer of Egnyte. “Organizations must pay extra attention to what data is used with these AI tools, whether third parties like OpenAI, PaLM, or an internal LLM that the company may use in-house.”?Review genAI policies around privacy, data protection, and acceptable use. Many organizations require submitting requests and approvals from data owners before using data sets for genAI use cases. Consult with risk, compliance, and legal functions before using data sets that must meet GDPR, CCPA, PCI, HIPAA, or other data compliance standards. Data policies must also consider the data supply chain and responsibilities when working with third-party data sources. “Should a security incident occur involving data that is protected within a certain region, vendors need to be clear on both theirs and their customers’ responsibilities to properly mitigate it, especially if this data is meant to be used in AI/ML platforms” says Jozef de Vries, chief product engineering officer of EDB.

Will AI Replace Consultants? Here’s What Business Owners Say.

“Most consultants aren’t actually that smart," said Michael Greenberg of Modern Industrialists. “They’re just smarter than the average person.” But he reckons the average machine is much smarter. “Consultants generally do non-creative tasks based around systematic analysis, which is yet another thing machines are normally better at than humans.” Greenberg believes some consultants, “doing design or user experience, will survive,” but “the run of the mill accounting degree turned business advisor will not.” Someone who has “replaced all of [her] consultants with ChatGPT already, and experienced faster growth,” is Isabella Bedoya, founder of MarketingPros.ai. However, she thinks because “most people don't know how to use AI, savvy consultants need to leverage it to become even more powerful, effective and efficient for their clients” and stay ahead of their game. Heather Murray, director at Beesting Digital, thinks the inevitable replacement of consultants is down to quality. “There are so many poor quality consultants that rely rigidly on working their clients through set frameworks, regardless of the individual’s needs. AI could do that easily.”?

Effective Code Documentation for Data Science Projects

The first step to effective code documentation is ensuring it’s clear and concise. Remember, the goal here is to make your code understandable to others – and that doesn’t just mean other data scientists or developers. Non-technical stakeholders, project managers, and even clients may need to understand what your code does and why it works the way it does. To achieve this, you should aim to use plain language whenever possible. Avoid jargon and overly complex sentences. Instead, focus on explaining what each part of your code does, why you made the choices you did, and what the expected outcomes are. If there are any assumptions, dependencies, or prerequisites for your code, these should be clearly stated. Remember, brevity is just as important as clarity. ... Data science projects are often dynamic, with models and data evolving over time. This means that your code documentation needs to be equally dynamic. Keeping your documentation up to date is critical to ensuring its usefulness and accuracy. A good practice here is to treat your documentation as part of your code, updating it as you modify or add to your code base.

Breaking down the language barrier: How to master the art of communication

Exactly how can cyber professionals go about improving their communication skills? According to Shapely, many people prefer to take short online learning courses. On-the-job coaching or mentorships are other popular upskilling strategies, providing quick and cost-effective practical learning opportunities. For those still early in their cybersecurity career, there is the option of building communication skills as part of a university degree. According to Kudrati, who teaches part-time at La Trobe University, many cybersecurity students must complete one subject on professional skills as part of their course. “This helps train students’ presentation skills, requiring them to present in front of lecturers and classmates as if they’re customers or business teams,” he says. Homing in on communication skills at university or early on in a cybersecurity professional’s career is also encouraged by Pearlson. In a study she conducted into the skills of cybersecurity professionals, she found that while communication skills were in demand, they were lacking, particularly among those in entry roles.?

4 core AI principles that fuel transformation success

Around 86% of software development companies are agile, and with good reason. Adopting an agile mindset and methodologies could give you an edge on your competitors, with companies that do seeing an average 60% growth in revenue and profit as a result. Our research has shown that agile companies are 43% more likely to succeed in their digital projects. One reason implementing agile makes such a difference is the ability to fail fast. The agile mindset allows teams to push through setbacks and see failures as opportunities to learn, rather than reasons to stop. Agile teams have a resilience that’s critical to success when trying to build and implement AI solutions to problems. Leaders who display this kind of perseverance are four times more likely to deliver their intended outcomes. Developing the determination to regroup and push ahead within leadership teams is considerably easier if they’re perceived as authentic in their commitment to embed AI into the company. Leaders can begin to eliminate roadblocks by listening to their teams and supporting them when issues or fears arise. That means proactively adapting when changes occur, whether this involves more delegation, bringing in external support, or reprioritizing resources.

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