Vulnerability detection in Java source code using a quantum convolutional neural network with self-attentive pooling, deep sequence, and graph-based hybrid feature extraction | Scientific Reports
AI Revolution: 3 Underdog Tech Stocks Powering The Future
InvestorPlace - Stock Market News, Stock Advice & Trading Tips
The three tech stocks here can each be viewed as underdogs powering the future. Some will immediately dismiss this list for the fact that the first company discussed in the article is one of the largest in the world. It's almost impossible to call it an underdog in any sense.
Yet, when it comes to the AI revolution, that is arguably very much the case. The arguments favoring the other two companies as underdogs rely on different arguments.
Regardless, each is maligned in its own right in relation to AI. That makes each an arguable underdog and who doesn't like an underdog? Most of us have felt like an underdog at some point in our lifetimes. Many of the greatest stories ever told rely on the underdog beating seemingly insurmountable odds.
These stocks each exemplify some of those characteristics as it relates to AI.
Apple (AAPL)
Source: Moab Republic / Shutterstock
Apple (NASDAQ:AAPL) can be considered an underdog when it comes to AI. The other dominant Silicon Valley Tech stocks have certainly benefited from AI much earlier and to a greater degree than Apple.
Although Apple was late to the game, we now know much more about its plans for AI following the Worldwide Developers Conference from June.
One of the primary reasons to root for Apple as it implements AI is the company's focus on privacy and security. Apple will prioritize on-device machine learning rather than sending user data to the cloud.
It also seems that Apple's AI focus centers on leveraging generative AI to improve user experience. That's unsurprising given Apple's extreme focus on user experience historically.
The point here is that Apple is differentiating itself in relation to AI. Many of the big tech companies are focused on maximizing AI to extract as much data as possible from the respective user bases.
Apple appears to have differentiated itself in that regard.
It remains to be seen whether that unique take on AI privacy will be well-received, but the application of AI is certainly expected to spike flagging iPhone sales. That is another strong reason to consider investing in Apple, as it attempts to move past its AI underdog status.
Tesla (TSLA)
Source: kovop / Shutterstock.Com
Tesla (NASDAQ:TSLA) is simultaneously an underdog stock and one representing the future AI revolution.
Share prices are still down year-to-date, despite a period of resurgence that began in early July. Tesla has long been associated with the application of artificial intelligence to its vehicles.
The autopilot feature in its vehicles isn't fully autonomous, however, the company continues to invest heavily in the development of AI for that purpose. When it has to delay that development, as it did in relation to the robotaxi, shares often suffer.
Regardless, Tesla is one of the more prominent AI stocks certainly in the automotive sector. Yet, it is also an underdog as pressure continues to ripple across the EV sector.
The company no longer enjoys the margins it once did, as competition increases sector-wide. Tesla is now engaged in a volume war that will rely on lower pricing moving forward.
Meanwhile, it is also simultaneously representing the future of AI through its Optimus robots. They represent the other side of AI at Tesla beyond the vehicle autonomy. In time, they may assemble Tesla's vehicles.
Alibaba (BABA)
Source: zhu difeng / Shutterstock.Com
Alibaba (NYSE:BABA) continues to play the underdog role in the Chinese e-Commerce space, even as other stocks, including PDD Holdings (NASDAQ:PDD), have stolen some of Alibaba's former clout.
Alibaba, once the king of Chinese eCommerce, has become an underdog. However, Alibaba is no slouch when it comes to AI. It's the combination of that underdog status — plus the strength of platforms like Temu — that makes it so potentially potent.
Alibaba has invested heavily in AI across its business. For example, the company utilizes natural language processing for its customer service bots, computer vision for product recommendation and machine learning and Big Data to better understand user behavior.
Beyond that, Alibaba uses AI to improve its logistical capability. The technology helps the company optimize delivery routes and better analyze demand fluctuation. And the list goes on and on in relation to Alibaba's AI development.
Remember, Alibaba is still the largest player in the Chinese e-commerce market with a 40% share. It's been temporarily counted out and that is creating a big opportunity especially as the company continues to develop AI.
On the date of publication, Alex Sirois did not have (either directly or indirectly) any positions in the securities mentioned in this article. The opinions expressed in this article are those of the writer, subject to the InvestorPlace.Com Publishing Guidelines.
On the date of publication, the responsible editor did not have (either directly or indirectly) any positions in the securities mentioned in this article.
Alex Sirois is a freelance contributor to InvestorPlace whose personal stock investing style is focused on long-term, buy-and-hold, wealth-building stock picks. Having worked in several industries from e-commerce to translation to education and utilizing his MBA from George Washington University, he brings a diverse set of skills through which he filters his writing.
More From InvestorPlaceThe post AI Revolution: 3 Underdog Tech Stocks Powering the Future appeared first on InvestorPlace.
What Does AI Mean For The Future Of Insights And The Market Research Industry?
Abigail Stuart, with 20+ years in brand and market research, drives innovation and champions AI. Connect on LinkedIn.
gettyI've been working in market research for my entire career spanning a 30-year period, including leading large teams and building my own successful agency. I'm someone who is curious and open to change, so I've always tried to stay at the forefront of innovations, playing an active role in shaping our industry.
But if I'm completely honest, not a lot has changed in the world of market research over that period. The biggest shift came with the arrival of the internet and the wholesale shift of market research surveys being conducted online, capitalizing on speed, reach and cost savings. However, research methodologies are pretty much the same today as they were 30 years ago—we gather people's opinions by asking them about their past behaviors, attitudes and needs via surveys, interviews and focus groups. Sometimes we use mobile devices and virtual platforms to speak to people, but the instrument and design remain pretty much unchanged.
Then along comes generative AI (GenAI) in the form of large language models such as ChatGPT 4.0 and other tools capable of generating human-like output in text, visual and audio forms. I predict with absolute certainty that GenAI will fundamentally change the way insights are generated—in fact, it is already offering unprecedented opportunities for innovation.
But the big question is where, as leaders, can we start to harness the power of GenAI for insight generation?
I'd like to put forward a scenario planning framework to take us on an AI innovation journey, starting with leveraging new technologies to enhance current practices, moving to the creation of new methodologies, then toward a paradigm shift to radically redefine how insights are generated.
Scenario 1: Optimize: Streamline existing market research processes.There are a lot of mundane tasks to complete in a market research project—from fieldwork updates, data cleaning and proofreading and other aspects of quality control. All of this is important, but it adds to the cost while reducing time for interpretation and storytelling.
This scenario is an easy first win for insight leaders, with a focus on leveraging GenAI to make market research processes more efficient. Insight leaders need to prepare to automate up to 80% of what they do—because if they do not, someone else will and they will lose competitiveness.
Key aspects include faster turnaround, reduced manual effort, automation of repetitive tasks, and improvement in data processing speeds. For example:
In this context, AI is applied to existing datasets to enhance its strategic value. It helps companies extract more value from the vast digital datasets they collect—data that is often unstructured and complex, making it difficult to process and analyze using traditional methods. For example:
In this scenario, the focus shifts to transformational innovation. The major shift to prepare for is a move away from traditional surveys that collect opinions via closed questions and rating and ranking scales to undertake "qualitative research at scale." GenAI, with its ability to understand, generate and manipulate human language, will transform qualitative research by making it scalable in ways previously only applicable to quantitative methods.
Imagine a client calls you on Friday afternoon needing urgent insights into their new global ad campaign by Monday morning. They want quantitative data for confident decision-making, but they also want rich qualitative insights from consumers across multiple markets. With GenAI this is now possible, here's how:
In this scenario, GenAI will be used not just to analyze data but to foresee and shape future market landscapes. In this transformative quadrant, we are only limited by our own imaginations of what is possible. The potential applications of AI are vast:
We are at a pivotal moment for the market research industry. If we embrace GenAI and drive toward transformational innovation, the industry will continue to thrive. If we fail to adapt, we risk technology disruptors infiltrating our sector and pushing seasoned professionals to the sidelines.
Start small, but act today by embracing GenAI to make market research more efficient through automation. Then shift your attention to augmenting existing methodologies with new tools for data collection and analysis. Keep an eye on the grand prize, where AI transforms insights generation into an always-on service, generating real-time insights from real and synthetic data, with the ability to predict consumer behavior to inform investment decisions.
By staying curious, open to change and committed to continuous learning, we can harness AI's full potential and lead the market research industry into a new era of excellence.
Forbes Agency Council is an invitation-only community for executives in successful public relations, media strategy, creative and advertising agencies. Do I qualify?
The Transformative Impact Of AI
Artificial Intelligence (AI) has emerged as a game-changer in the financial services industry, revolutionising traditional banking practices and unlocking new opportunities for innovation and efficiency. In this article, we explore the transformative impact of AI on various facets of financial services and its potential to reshape the future of banking.
Enhanced Customer Experience
AI-powered chatbots and virtual assistants are revolutionising customer interactions, providing personalised assistance, and streamlining processes such as account inquiries, transaction monitoring, and customer support. By leveraging natural language processing (NLP) and machine learning algorithms, financial institutions can deliver seamless, omnichannel experiences that enhance customer satisfaction and loyalty.
For example, Bank of America's virtual assistant, Erica, is utilising AI to provide an intuitive customer experience. Erica helps users manage their accounts, monitor transactions, and even receive bill reminders by integrating a search bar functionality into the app. These modifications point to a widespread trend of using AI to improve user interaction and satisfaction in banking applications.
Risk Management and Fraud Detection
AI algorithms have become indispensable tools for identifying and mitigating risks in the financial sector. From detecting fraudulent transactions to assessing creditworthiness and monitoring market trends, AI-powered risk management systems enable banks to make data-driven decisions in real-time, reducing operational costs and safeguarding against financial crimes.
Predictive Analytics and Financial Planning
AI-driven predictive analytics empower banks to gain actionable insights from vast volumes of data, enabling more accurate forecasting and proactive decision-making. By analysing customer behaviour, market trends, and economic indicators, financial institutions can offer personalised financial advice, optimise investment strategies, and anticipate future market conditions with greater precision.
Process Automation and Operational Efficiency
AI technologies such as robotic process automation (RPA) are automating routine tasks and streamlining back-office operations, reducing manual errors and operational costs. By automating data entry, document processing, and compliance tasks, banks can optimise resource allocation, improve process efficiency, and allocate human capital to higher-value activities.
Algorithmic Trading and Investment Management
In the realm of investment banking, AI algorithms are revolutionising trading strategies and portfolio management. Through algorithmic trading platforms and robo-advisors, financial institutions can execute trades at lightning speed, optimise asset allocation, and tailor investment portfolios to individual risk profiles, delivering superior returns and driving investor confidence.
Take, for example, JPMorgan Chase's Moneyball" tool. This AI-driven tool provides portfolio managers with valuable insights into market trends and their own patterns of investment behaviour. Moneyball helps identify potential biases in investment practices by analysing extensive amounts of data. This allows managers to make better, more objective, data-informed decisions.
Regulatory Compliance and Governance
AI-powered regulatory compliance solutions are helping banks navigate an increasingly complex regulatory landscape. By automating compliance monitoring, regulatory reporting, and audit processes, AI technologies ensure adherence to regulatory requirements, mitigate compliance risks and enhance transparency and accountability across the organisation.
Ethical Considerations and Bias Mitigation
While AI offers immense innovation potential,it has also raised ethical concerns around data privacy, algorithmic bias, and fairness. Financial institutions must prioritise ethical AI practices, including transparent algorithmic decision-making, data governance, and bias mitigation strategies, to build trust with customers and stakeholders and ensure responsible AI deployment.
AI is reshaping the future of banking, offering unprecedented opportunities for innovation, efficiency, and customer-centricity. By harnessing the power of AI technologies across various facets of financial services, banks can unlock new revenue streams, mitigate risks, and deliver superior value to customers in an increasingly digital and data-driven world. As AI continues to evolve, financial institutions must embrace a culture of innovation and collaboration to stay at the forefront of the AI revolution and drive sustainable growth and competitiveness in the dynamic landscape of financial services.
Comments
Post a Comment