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Sanford firmly believes that AI is not just a buzzword but a game changer that holds the key to unlocking extraordinary opportunities within the real estate sphere.

AI is not about replacing real estate professionals; its about enhancing their abilities and the overall customer journey, asserts Sanford, emphasizing his commitment to leveraging AI as a collaborative tool rather than a divisive force in the industry. Unlike those hesitant to embrace change, Sanford recognizes the immense potential AI brings to the table and views it as an indispensable asset that can elevate agents proficiency and effectiveness.

I am an entrepreneur at heart, which means I think like a true entrepreneur, its less about P&L. Im not building a business to fund a lifestyle. Most entrepreneurs would rather be broke than have a mediocre business thats technically profitable, he says. Its this mindset, what he calls the mindset of a person that builds a start up that encourages him to radical things [such as investing in AI], he says. You realize that you can crash and burn a number of times while building something that finally gets traction.

However, Sanford has no plans to crash and burn with the AI-driven solutions tailored explicitly to cater to the ever-changing demands of the modern real estate market. Were starting to make investments into various companies on the edges. We want to create opportunities for people to merge their new ideas inside the city of eXp that would benefit agents, brokers and staff. That includes eXp Ventures, to foster innovation. How do we take from companies that have done well and innovate in a modern way?

By harnessing the power of machine-learning algorithms, eXp Realtys agents can now gain unprecedented insights into market trends, accurately predict property values, and efficiently match buyers with their dream homes.

Weve got a number of instances around the company, and were going to use other instances of either generative AI or image AI. We are already doing some image AI, says Sanford. Were already working AI into our search solutions, like Zoocasa and others. So, youll be able to use natural language search when searching for property. So, the stuff that Zillows doing, were incorporating, he says.

Real estate agents are going to get seriously disrupted by AI, says Sanford, but not in the value of the real estate agent, but more in the way things are done. Think about the [possibility] that lead follow up and nurturing campaigns will be managed by AI in the future. Look at platforms like Synthesia, [an AI video generator]. At eXp, we have a partnership with Blended Sense, [a content creation platform], so agents can do a video using Blended Sense [then upload] that into Synthesia, says Sanford.

The agent can then add in content about their local community thats generated by ChatGPT-4 and pump it into Synthesia. They can self-narrate with their voice using an AI-generated version of themselves with AI-generated content. And in some cases, the consumer wont even know it wasnt the agent actually providing that information, he says.

Sanford envisions a future where AI-driven chatbots effortlessly handle routine inquiries, freeing up valuable time for agents to focus on building deeper connections with clients and offering tailored guidance throughout the real estate journey. The true essence of real estate lies in nurturing meaningful relationships, Sanford says, and AI should serve as a seamless enabler rather than an intrusive barrier in achieving that.

While some may view AI as an accessory, Sanford passionately believes that integrating AI is essential in fortifying the industrys foundation for generations to come. He envisions a day when AI algorithms will go beyond predictive analytics and assist agents in curating personalized property recommendations that align perfectly with their clients preferences and lifestyles.

Moreover, Sanford is not one to rest on his laurels; he relentlessly invests in research and development to push the boundaries of what AI can accomplish for the real estate world. Sanfords commitment to staying ahead of the technological curve is driven by his belief that embracing AI wholeheartedly is not an option but a necessity to remain relevant in an ever-accelerating digital era.

When it comes to integrating AI into your brokerage, Sanford sums it up this way: The reality is that it doesnt matter what the controversy is. Its literally those who dont use AI will work for people who use AI.

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eXp's Glenn Sanford on AI's transformative impact in real estate - HousingWire

The early innings of the artificial intelligence trade may be over, but Citigroup is staying positive on the tech subsector, viewing cash flows as the key to unlocking the winners of the next phase. "In sum, our message is not to be overly deterred by the significant year-to-date move in profitable AI stocks," the bank said in a Friday note to clients. "Medium- to long-term opportunities still exist as the AI theme has an accelerating growth trajectory and attractive [free cash flow] dynamics that should further improve from here." So far this year, anything connected to AI has seen a significant uptick in valuation, with Nvidia shares leading the pack, surging more than 200%. While the jaw-dropping price action may suggest AI is no longer an early trade, Citi reiterated that the "initial positive thesis" looks intact and warned investors to avoid overlooking free cash flows. Citi expects many names to meet accelerated growth expectations and views free cash flows as "increasingly compelling." "Profitable stocks within this theme are already impressive cash generating machines," the bank wrote. "Recent AI developments should accentuate this characteristic and push FCF margins and growth to new highs." Given this setup, Citi screened for AI-related stocks expected to outpace market growth expectations and experience an uptick in free cash flow margins. Here are some of the stocks that made the cut: Amazon has the highest consensus expectation of more than 48% growth over the long term. Shares have gained almost 54% this year as Wall Street rotates back into technology stocks following the slump in 2022. Some investors have viewed the e-commerce giant as lagging behind its peers in the AI race. During an i nterview with CNBC this month, CEO Andy Jassy soothed some of those concerns, reiterating Amazon's plan to invest in AI across segments. Earlier this year , Amazon also unveiled a generative AI service called Bedrock for its Amazon Web Services unit, allowing clients to use language models to create their own chatbots and image-generation services. Competing chatbot heavyweight Alphabet also made the cut. Shares of the Google parent and Bard creator have rallied 38% as it battles it out with Microsoft -backed OpenAI's ChatGPT. Consensus estimates peg long-term growth at more than 17%, with a near-term free cash flow margin of nearly 24%. GOOGL YTD mountain Alphabet shares in 2023 A handful of financial stocks were also included in Citi's screen. Mastercard offers the greatest near-term free cash flow yield of the group, at 48.4%. Its long-term consensus growth estimate hovers around 19%. Shares have gained about 15% year to date. Ford Motor , Match Group and ServiceNow also made the list. CNBC's Michael Bloom contributed reporting.

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Citi stays positive on A.I. theme and lays out the key to finding ... - CNBC

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Antony Blinken & Gina Raimondo: To shape the future of AI, we must ... - Financial Times

The moment that generative AI applications hit the market, it changed the pace of business not only for security teams, but for cybercriminals too. Today, not embracing AI innovations can mean falling behind your competitors and putting your cyber defense at a disadvantage against cyberattacks powered by AI. But when discussing how AI will or won't impact cybercrime, it's important that we look at things through a pragmatic and sober lens not feeding into hype that reads more like science fiction.

Today's AI advancements and maturity signal a significant leap forward for enterprise security. Cybercriminals can't easily match the size and scale of enterprises' resources, skills, and motivation, making it harder for them to keep up with the current speed of AI innovation. Private venture investment in AI exploded to $93.5 billion in 2021 the bad guys don't have that level of capital. They also don't have the manpower, computing power, and innovations that affords commercial companies or government more time and opportunity to fail quick, learn fast, and get it right first.

Make no mistake, though: Cybercrime will catch up. This is not the first time the security industry has had a brief edge when ransomware started driving more defenders to adopt endpoint detection and response technologies, attackers needed some time to figure out how to circumvent and evade those detections. That interim "grace period" gave businesses time to better shield themselves. The same applies now: Businesses need to maximize on their lead in the AI race, advancing their threat detection and response capabilities and leveraging the speed and precision that current AI innovations afford them.

So how is AI changing cybercrime? Well, it won't change it substantially anytime soon, but it will scale it in certain instances. Let's take at a look at where malicious use of AI will and won't make the most immediate impact.

In recent months, we've seen claims regarding various malicious use cases of AI, but just because a scenario is possible does not make it probable. Take fully automated malware campaigns, for example logic says that it is possible to leverage AI to achieve that outcome, but given that leading tech companies have yet to pioneer fully automated software development cycles, it's unlikely that financially constrained cybercrime groups will achieve this sooner. Even partial automation can enable the scaling of cybercrime, however, a tactic we've already seen used in Bazar campaigns. This is not an innovation, but a tried-and-true technique that defenders are already taking on.

Another use case to consider is AI-engineered phishing attacks. Not only is this one possible, but we're already beginning to see these attacks in the wild. This next generation of phishing may achieve higher levels of persuasiveness and click-rate, but a human-engineered phish and AI-engineered phish still drive toward the same goal. In other words, an AI-engineered phish is still a phish searching for a click, and it requires the same detection and response readiness.

However, while the problem remains the same, the scale is vastly different. AI acts as a force multiplier to scale phishing campaigns, so, if an enterprise is seeing a spike in inbound phishing emails and those malicious emails are significantly more persuasive then it's likely looking at a high click-rate probability and potential for compromise. AI models can also increase targeting efficacy, helping attackers determine who is the most susceptible target for a specific phish within an organization and ultimately reaching a higher ROI from their campaigns. Phishing attacks have historically been among the most successful tactics that attackers have used to infiltrate enterprises. The scaling of this type of attack emphasizes the critical role that EDR, MDR, XDR, and IAM technologies play in detecting anomalous behavior before it achieves impact.

AI poisoning attacks, in other words programmatically manipulating the code and data on which AI models are built, may be the "holy grail" of attacks for cybercriminals. The impact of a successful poisoning attack could range anywhere from misinformation attempts to Die Hard 4.0. Why? Because by poisoning the model, an attacker can make it behave or function in whatever way they want, and it's not easily detectable. However, these attacks aren't easy to carry out they require gaining access to the data the AI model is training on at the time of training, which is no small feat. As more models become open source, the risk of these attacks will increase, but it will remain low for the time being.

While it's important to separate hype from reality, it's also important to ensure we're asking the right questions about AI's impact on the threat landscape. There are lots of unknowns regarding AI's potential how it may change adversaries' goals and objectives is one we mustn't overlook. It remains unknown how new abilities may help serve new purposes for adversaries and recalibrate their motives.

We may not see an immediate spike in novel AI-enabled attacks, but the scaling of cybercrime thanks to AI will have a substantial impact on organizations that aren't prepared. Speed and scale are intrinsic characteristics of AI, and just as defenders are seeking to benefit from them, so are attackers. Security teams are already understaffed and overwhelmed seeing a spike in malicious traffic or incident response engagements is a substantial weight added onto their workload.

This reaffirms more than ever the need for enterprises to invest in their defenses, using AI to drive speed and precision in their threat detection and response capabilities. Enterprises that take advantage of this "grace period" will find themselves much more prepared and resilient for the day attackers actually do catch up in the AI cyber race.

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Are AI-Engineered Threats FUD or Reality? - Dark Reading

Jinjun Xiongs years of experience with artificial intelligence (AI) are making a dramatic impact at UB.

SUNY Empire Innovation Professor of Computer Science and Engineering, Xiong is scientific director and co-director of the AI Institute for Exceptional Education, a national institute developing artificial intelligence systems that identify and assist young children with speech and/or language processing challenges. It was established earlier this year with a five-year, $20 million grant from the National Science Foundation.

Xiong also serves as co-director of UBsInstitute for Artificial Intelligence and Data Science (IAD),where he connects investigators including clinical and translational researchers with the power of AI.

These efforts include:

I am also always looking for new ideas for how we can make the IAD platform more useful and accessible for all UB investigators, Xiong says.

He believes it is important for researchers and the public to understand artificial intelligence, and the ways in which it is changing our world. In a Q&A with UBNow, Xiong discusses the impact of AI on research now and in the future, and analyzes how it will affect health care.

AI is already impacting clinical research in multiple ways, such as medical imagining analyses for skin cancer detection, MRI imaging segmentation, clinical trials data understanding, wearable sensors to improve patient monitoring the list just goes on and on. The future of clinical practices will incorporate more and more intelligent solutions enabled by more efficient and intelligent algorithms, all aiming to improve the patient quality of care. One such example is the growing capabilities of AI, especially the recent amazing results from generative AI like ChatGPT, where it is conceivable that AI-augmented agents such as chatbots can help with providing more accessible and higher-quality health literacy for patients.

To some degree, every future professional needs to understand a bit about AI and computing, by either talking to AI experts/researchers or learning online to gain a general understanding of how AI works, and what AI can do and cannot do right now and even in the near future. With that basic understanding, people working in a particular domain like medicine can revisit their daily practices and think out of the box about where AI can help in their current practice flows, and then engage with an AI expert to co-imagine and then co-design a possible AI-driven solution.

The public should realize that the impact of AI to health care is real and inevitable. There is always an ethical and moral issue around AI in health care, as it may potentially remove autonomy from humans. But that is exactly why the public should be aware of the technology so they can be part of the conversation to find meaningful solutions. I believe the voices of the public should be heard in charting a new direction for humankind with AI.

The power of AI can only become real when it is applied to solve a particular domain problem.

For more information on IAD research initiatives, write to Xiong atjinjun@buffalo.edu.

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QA Qa Xiong - UBNow: News and views for UB faculty and staff - University at Buffalo