The next wave of AI wont be driven by LLMs Heres what investors should focus on

The contributed papers cover some of the more challenging open questions in the area of Embodied and Enactive AI and propose some original approaches. Scarinzi and Cañamero argue that “artificial emotions” are a necessary tool for an agent interacting with the environment. Hernandez-Ochoa point out the potential importance and usefulness of the evo-devo approach for artificial emotional systems. The problem of anchoring a symbolic description to a neural encoding is discussed by Katz et al., who propose a “neurocomputational controller” for robotic manipulation based on a “neural virtual machine” (NVM). The NVM encodes the knowledge of a symbolic stacking system, but can then be further improved and fine-tuned by a Reinforcement Learning procedure.

They are sub-par at cognitive or reasoning tasks, however, and cannot be applied across disciplines. “AI systems of the future will need to be strengthened so that they enable humans to understand and trust their behaviors, generalize to new situations, and deliver robust inferences. Neuro-symbolic AI, which integrates neural networks with symbolic representations, has emerged as a promising approach to address the challenges of generalizability, interpretability, and robustness. In conclusion, the EXAL method addresses the scalability and efficiency challenges that have limited the application of NeSy systems.

Business processes that can benefit from both forms of AI include accounts payable, such as invoice processing and procure to pay, and logistics and supply chain processes where data extraction, classification and decisioning are needed. In the landscape of cognitive science, understanding System 1 and System 2 thinking offers profound insights into the workings of the human mind. According to psychologist Daniel Kahneman, “System 1 operates automatically and quickly, with little or no effort and no sense of voluntary control.” It’s adept at making rapid judgments, which, although efficient, can be prone to errors and biases. Examples include reading facial expressions, detecting that one object is more distant than another and completing phrases such as “bread and…”

• One difficulty is that we cannot say for sure the precise way that people reason.
• For those of you familiar with the history of AI, there was a period when the symbolic approach was considered top of the heap.
• The act of having and using a bona fide method does not guarantee a correct response.
• With the emergence of symbolic communication, society has become the subject of PC via symbol emergence.
• The approach provided a Bayesian view of symbol emergence including a theoretical guarantee of convergence.
• They are also better at explaining and interpreting the AI algorithms responsible for a result.

There needs to be increased investment in research and development of reasoning-based AI architectures like RAR to refine and scale these approaches. Industry leaders and influencers must actively promote the importance of logical reasoning and explainability in AI systems over predictive generation, particularly in high-stakes domains. Finally, collaboration between academia, industry and regulatory bodies is crucial to establish best practices, standards and guidelines that prioritize transparent, reliable and ethically aligned AI systems. The knowledge graph used can also be expanded to include nuanced human expertise, allowing the AI to leverage documented regulations, policies or procedures and human tribal knowledge, enhancing contextual decision-making.

Editorial: Novel methods in embodied and enactive AI and cognition

This is an approach attempting to bridge “symbolic descriptions” with data-driven approaches. In Hinrichs et al., the authors show via a thorough data analysis how “meaning,” as it is understood by us humans in natural language, is actually an unstable ground for symbolic representations, as it shifts from language to language. An early stage controller inspired by Piaget’s schemas is proposed by Lagriffoul.

These core data tenets will ensure that what is being fed into your AI models is as complete, traceable and trusted as it can be. Not doing so creates a huge barrier to AI implementation – you cannot launch something that doesn’t perform consistently. We have all heard about the horror of AI hallucinations and spread of disinformation. symbolic ai With a generative AI program built on a shaky data foundation, the risk is simply much too high. A lack of vetted, accurate data powering generative AI prototypes is where I suspect the current outcry truly comes from instead of the technologies powering the programs themselves where I see some of the blame presently cast.

One of the most eye-catching examples was a system called R1 that, in 1982, was reportedly saving the Digital Equipment Corporation US$25m per annum by designing efficient configurations of its minicomputer systems. Adrian Hopgood has a long-running unpaid collaboration with LPA Ltd, creators of the VisiRule tool for symbolic AI. As AI technologies automate legal research and analysis, it’s easy to succumb to rapid judgments (thinking fast) — assuming the legal profession will be reshaped beyond recognition. Lawyers frequently depend on quick judgments to assess cases, but detailed analysis is equally important, mirroring how thinking slow was vital in uncovering the truth at Hillsborough.

Traditional learning methods in NeSy systems often rely on exact probabilistic logic inference, which is computationally expensive and needs to scale better to more complex or larger systems. This limitation has hindered the widespread application of NeSy systems, as the computational demands make them impractical for many real-world problems where scalability and efficiency are critical. Looking ahead, the integration of neural networks with symbolic AI will revolutionize the artificial intelligence landscape, offering previously unattainable capabilities.

The FEP is not only concerned with the activities of individual brains but is also applicable to collective behaviors and the cooperation of multiple agents. Researchers such as Kaufmann et al. (2021); Levchuk et al. (2019); Maisto et al. (2022) have explored frameworks for realizing collective intelligence and multi-agent collaboration within the context of FEP and active inference. However, the theorization of language emergence based on FEP has not yet been accomplished.

People are taught that they must come up with justifications and explanations for their behavior. The explanation or justification can be something they believe happened in their heads, though maybe it is just an after-the-fact concoction based on societal and cultural demands that they provide cogent explanations. We must take their word for whatever they proclaim has occurred inside their noggin. When my kids were young, I used to share with them the following example of inductive reasoning and deductive reasoning.

This caution is echoed by John J. Hopfield and Geoffrey E. Hinton, pioneers in neural networks and recipients of the 2024 Nobel Prize in Physics for their contributions to AI. Contract analysis today is a tedious process fraught with the possibility of human error. Lawyers must painstakingly dissect agreements, identify conflicts and suggest optimizations — a time-consuming task that can lead ChatGPT to oversights. Neuro-symbolic AI could addresses this challenge by meticulously analyzing contracts, actively identifying conflicts and proposing optimizations. By breaking down problems systematically, o1 mimics human thought processes, considering strategies and recognizing mistakes. This ultimately leads to a more sophisticated ability to analyze information and solve complex problems.

Or at least it might be useful for you to at some point share with any youngsters that you happen to know. Warning to the wise, do not share this with a fifth grader since they will likely feel insulted and angrily retort that you must believe them to be a first grader (yikes!). I appreciate your slogging along with me on this quick rendition of inductive and deductive reasoning. Time to mull over a short example showcasing inductive reasoning versus deductive reasoning. We normally expect scientists and researchers to especially utilize deductive reasoning. They come up with a theory of something and then gather evidence to gauge the validity of the theory.

Contributed articles

For my comprehensive coverage of over fifty types of prompt engineering techniques and tips, see the link here. The customary means of achieving modern generative AI involves using a large language model or LLM as the key underpinning. One other aspect to mention about the above example of deductive reasoning about the cloud and temperature is that besides a theory or premise, the typical steps entail an effort to apply the theory to specific settings.

Our saturated mindset states that all AI must start with data, yet back in the 1990s, there wasn’t any data and we lacked the computing power to build machine learning models. In standard deep learning, back-propagation calculates gradients to measure the impact of the weights on the overall loss so that the optimizers can update the weights accordingly. In the agent symbolic learning framework, language gradients play a similar role. The agent symbolic learning framework implements the main components of connectionist learning (backward propagation and gradient-based weight update) in the context of agent training using language-based loss, gradients, and weights. Existing optimization methods for AI agents are prompt-based and search-based, and have major limitations. Search-based algorithms work when there is a well-defined numerical metric that can be formulated into an equation.

Language models excel at recognizing patterns and predicting subsequent steps in a process. However, their reasoning lacks the rigor required for mathematical problem-solving. The symbolic engine, on the other hand, is based purely on formal logic and strict rules, which allows it to guide the language model toward rational decisions. Generative AI, powered by large language models (LLMs), excels at understanding context and natural language processing.

How AI agents can self-improve with symbolic learning

Then comes a period of rapid acceleration, where breakthroughs happen quickly and the technology begins to change industries. But eventually, every technology reaches a plateau as it hits its natural limits. This is why AI experts like Gary Marcus have been calling LLMs “brilliantly stupid.” They can generate impressive outputs but are fundamentally incapable of the kind of understanding and reasoning that would make them truly intelligent. The diminishing returns we’re seeing from each new iteration of LLMs are making it clear that we’re nearing the top of the S-curve for this particular technology. Drawing inspiration from Daniel Kahneman’s Nobel Prize-recognized concept of “thinking, fast and slow,” DeepMind researchers Trieu Trinh and Thang Luong highlight the existence of dual-cognitive systems. “Akin to the idea of thinking, fast and slow, one system provides fast, ‘intuitive’ ideas, and the other, more deliberate, rational decision-making,” said Trinh and Luong.

The advantage of the CPC hypothesis is its generality in integrating preexisting studies related to symbol emergence into a single principle, as described in Section 5. In addition, the CPC hypothesis provides a theoretical connection between the theories of human cognition and neuroscience in terms of PC and FEP. Language collectively encodes information about the world as observed by numerous agents through their sensory-motor systems. This implies that distributional semantics encode structural information about the world, and LLMs can acquire world knowledge by modeling large-scale language corpora.

Cangelosi et al. (2000) tackled the symbol grounding problem using an artificial cognitive system. Developmental robotics researchers studied language development models (Cangelosi and Schlesinger, 2014). Embodied cognitive systems include various sensors and motors, and a robot is an artificial human with a multi-modal perceptual system. Understanding the dynamics of SESs that realize daily semiotic communications will contribute to understanding the origins of semiotic and linguistic communications. This hybrid approach combines the pattern recognition capabilities of neural networks with the logical reasoning of symbolic AI. Unlike LLMs, which generate text based on statistical probabilities, neurosymbolic AI systems are designed to truly understand and reason through complex problems.

I mentioned earlier that the core design and structure of generative AI and LLMs lean into inductive reasoning capabilities. This is a good move in such experiments since you want to be able to compare apples to apples. In other words, purposely aim to use inductive reasoning on a set of tasks and use deductive reasoning on the same set of tasks. Other studies will at times use a set of tasks for analyzing inductive reasoning and a different set of tasks to analyze deductive reasoning. The issue is that you end up comparing apples versus oranges and can have muddled results.

Some would argue that we shouldn’t be using the watchword when referring to AI. The concern is that since reasoning is perceived as a human quality, talking about AI reasoning is tantamount to anthropomorphizing AI. To cope with this expressed qualm, I will try to be cautious in how I make use of the word. Just wanted to make sure you knew that some experts have acute heartburn about waving around the word “reasoning”. SingularityNET, which is part of the Artificial Super Intelligence Alliance (ASI) — a collective of companies dedicated to open source AI research and development — plans to expand the network in the future and expand the computing power available. You can foun additiona information about ai customer service and artificial intelligence and NLP. Other ASI members include Fetch.ai, which recently invested $100 million in a decentralized computing platform for developers.

The scarcity of diverse geometric training data poses limitations in addressing nuanced deductions required for advanced mathematical problems. Its reliance on a symbolic engine, characterized by strict rules, could restrict flexibility, particularly in unconventional or abstract problem-solving scenarios. Therefore, although proficient in “elementary” mathematics, AlphaGeometry currently falls short when confronted with advanced, ChatGPT App university-level problems. Addressing these limitations will be pivotal for enhancing AlphaGeometry’s applicability across diverse mathematical domains. The process of constructing a benchmark to evaluate LLMs’ understanding of symbolic graphics programs uses a scalable and efficient pipeline. It uses a powerful vision-language model (GPT-4o) to generate semantic questions based on rendered images of the symbolic programs.

We’re likely seeing a similar “illusion of understanding” with AI’s latest “reasoning” models, and seeing how that illusion can break when the model runs in to unexpected situations. Adding in these red herrings led to what the researchers termed “catastrophic performance drops” in accuracy compared to GSM8K, ranging from 17.5 percent to a whopping 65.7 percent, depending on the model tested. These massive drops in accuracy highlight the inherent limits in using simple “pattern matching” to “convert statements to operations without truly understanding their meaning,” the researchers write.

There’s not much to prevent a big AI lab like DeepMind from building its own symbolic AI or hybrid models and — setting aside Symbolica’s points of differentiation — Symbolica is entering an extremely crowded and well-capitalized AI field. But Morgan’s anticipating growth all the same, and expects San Francisco-based Symbolica’s staff to double by 2025. Using highly parallelized computing, the system started by generating one billion random diagrams of geometric objects and exhaustively derived all the relationships between the points and lines in each diagram. AlphaGeometry found all the proofs contained in each diagram, then worked backwards to find out what additional constructs, if any, were needed to arrive at those proofs.

Asjad is a Machine learning and deep learning enthusiast who is always researching the applications of machine learning in healthcare. The task description, input, and trajectory are data-dependent, which means they will be automatically adjusted as the pipeline gathers more data. The few-shot demonstrations, principles, and output format control are fixed for all tasks and training examples. The language loss consists of both natural language comments and a numerical score, also generated via prompting.

EXAL demonstrated superior scalability, maintaining a competitive accuracy of 92.56% for sequences of 15 digits, while A-NeSI struggled with a significantly lower accuracy of 73.27%. The capabilities of LLMs have led to dire predictions of AI taking over the world. Although current models are evidently more powerful than their predecessors, the trajectory remains firmly toward greater capacity, reliability and accuracy, rather than toward any form of consciousness. The MLP could handle a wide range of practical applications, provided the data was presented in a format that it could use. A classic example was the recognition of handwritten characters, but only if the images were pre-processed to pick out the key features.

This is because the language system has emerged to represent or predict the world as experienced by distributed human sensorimotor systems. This may explain why LLMs seem to know so much about the ‘world’, where ‘world’ means something like ‘the integration of our environments’. Therefore, it is suggested that language adopts compositionality based on syntax. In the conventional work using MHNG, the common node w in Figure 7 has been considered a discrete categorical variable.

• Should we keep on deepening the use of sub-symbolics via ever-expanding the use of generative AI and LLMs?
• But these more statistical approaches tend to hallucinate, struggle with math and are opaque.
• However, from the perspective of semiotics, physical interactions and semiotic communication are distinguishable.
• These lower the bars to simulate and visualize products, factories, and infrastructure for different stakeholders.
• Artificial intelligence (AI) spans technologies including machine learning and generative AI systems like GPT-4.

Because language models excel at identifying general patterns and relationships in data, they can quickly predict potentially useful constructs, but often lack the ability to reason rigorously or explain their decisions. Symbolic deduction engines, on the other hand, are based on formal logic and use clear rules to arrive at conclusions. They are rational and explainable, but they can be “slow” and inflexible – especially when dealing with large, complex problems on their own. Some proponents have suggested that if we set up big enough neural networks and features, we might develop AI that meets or exceeds human intelligence. However, others, such as anesthesiologist Stuart Hameroff and physicist Roger Penrose, note that these models don’t necessarily capture the complexity of intelligence that might result from quantum effects in biological neurons. By combining these approaches, the AI facilitates secondary reasoning, allowing for more nuanced inferences.

Rather than being post-communicative as in reference games, shared attention and teaching intentions were foundational in language development. Steels et al. proposed a variety of computational models for language emergence using categorizations based on sensory experiences (Steels, 2015). In their formulation, several types of language games were introduced and experiments using simulation agents and embodied robots were conducted.

Unlike traditional legal AI systems constrained by keyword searches and static-rule applications, neuro-symbolic AI adopts a more nuanced and sophisticated approach. It integrates the robust data processing powers of deep learning with the precise logical structures of symbolic AI, laying the groundwork for devising legal strategies that are both insightful and systematically sound. Innovations in backpropagation in the late 1980s helped revive interest in neural networks. This helped address some of the limitations in early neural network approaches, but did not scale well. The discovery that graphics processing units could help parallelize the process in the mid-2010s represented a sea change for neural networks. Google announced a new architecture for scaling neural network architecture across a computer cluster to train deep learning algorithms, leading to more innovation in neural networks.

“We were really just wanting to play with what the future of art could be, not only interactive, but ‘What is it?'” Borkson said. Not having attended formal art school meant that the two of them understood some things about it, but weren’t fully read on it. As a result, they felt greater license to play around, not having been shackled with the same restrictions on execution. The way that some people see Foo Foo and immediately think “That makes me happy,” is essentially the reaction they were going for in the early days. Now they are aiming for deeper experiences, but they always intend to imprint an experience upon someone.

Furthermore, CPC represents the first attempt to extend the concepts of PC and FEP by making language itself the subject of PC. Regarding the relationship between language and FEP, Kastel et al. (2022) provides a testable deep active inference formulation of social behavior and accompanying simulations of cumulative culture. However, even this approach does not fully embrace the CPC perspective, where language performs external representation learning utilizing multi-agent sensorimotor systems.

It follows that neuro-symbolic AI combines neural/sub-symbolic methods with knowledge/symbolic methods to improve scalability, efficiency, and explainability. It’s a component that, in combination with symbolic AI, will continue to drive transformative change in knowledge-intensive sectors. “Online spatial concept and lexical acquisition with simultaneous localization and mapping,” in IEEE/RSJ international conference on intelligent robots and systems, 811–818. “Exploring simple siamese representation learning,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 15750–15758. 4Note that the idea of emergent properties here is different from that often mentioned recently in the context of foundation models, including LLMs (Bommasani et al., 2021).

This prediction task requires knowledge of the scene that is out of scope for traditional computer vision techniques. More specifically, it requires an understanding of the semantic relations between the various aspects of a scene – e.g., that the ball is a preferred toy of children, and that children often live and play in residential neighborhoods. Knowledge completion enables this type of prediction with high confidence, given that such relational knowledge is often encoded in KGs and may subsequently be translated into embeddings. At Bosch Research in Pittsburgh, we are particularly interested in the application of neuro-symbolic AI for scene understanding. Scene understanding is the task of identifying and reasoning about entities – i.e., objects and events – which are bundled together by spatial, temporal, functional, and semantic relations.

Nevertheless, if we say that the answer is wrong and there are 19 digits, the system corrects itself and confirms that there are indeed 19 digits. A classic problem is how the two distinct systems may interact (Smolensky, 1991). A variety of computational models have been proposed, and numerous studies have been conducted, as described in Section 5, to model the cultural evolution of language and language acquisition in individuals. However, a computational model framework that captures the overall dynamics of SES is still necessary. The CPC aims to offer a more integrative perspective, potentially incorporating the pre-existing approaches to symbol emergence and emergent communication. For much of the AI era, symbolic approaches held the upper hand in adding value through apps including expert systems, fraud detection and argument mining.

Modern large language models are also vastly larger — with billions or trillions of parameters. Unlike o1, which is a neural network employing extended reasoning, AlphaGeometry combines a neural network with a symbolic reasoning engine, creating a true neuro-symbolic model. Its application may be more specialized, but this approach represents a critical step toward AI models that can reason and think more like humans, capable of both intuition and deliberate analysis.

KPMG 2024 CEO Outlook: Inaugural Africa edition African CEOs emulate business confidence and clear growth trajectory into 2025 The Business & Financial Times

Thomson Reuters report, ‘Tech, AI and the Law 2024’ provides a nuanced perspective on the integration of gen AI within the legal profession. The findings reveal an overwhelming 95% of Australian private practice legal professionals believe that while AI is no substitute for thorough legal work, it does serve as a powerful accelerator. In 2017, training a top-of-the-line model cost roughly $1,000, but by 2024 that cost had risen to around $200m, despite a rapid decline in computing costs. The driving force behind this surge in training costs is the astonishing growth in computing power required by LLMs. Dhahran, Saudi Arabia – Wa’ed Ventures, the $500 million venture capital fund wholly owned by Aramco, announces earmarking $100 million for early-stage AI investments, a bold move to support positioning the Kingdom as a global AI hub. “With 89% of African CEOs highlighting the impact of an aging workforce, it is critical that the younger talent pool is nurtured and developed to minimize the negative impact this could have on the sustainability of organizations.

These new technologies could bring a level of engagement that traditional methods struggle to match. Newsrooms that are willing to experiment with and adopt these technologies will be better positioned to engage audiences. ChatGPT While AI offers significant advancements and efficiencies across various sectors, it also comes with challenges such as regulatory uncertainties, data quality concerns and potential market overvaluation.

As the leader notes, over-reliance on AI for decisions such as hiring and performance evaluations without human involvement could leave employees feeling undervalued and disconnected, compromising the human aspect of work. The complexity and continued advancement of these solutions has come with increased concerns about privacy, security and social equity, posing potential risks to sensitive sectors such as healthcare and financial services. West Africa’s economic outlook in 2024 reflects cautious optimism among CEOs, with 60% confident in their country’s economic growth down from 73% last year. Key risks include trade regulation, operational issues, and rising cybercrime.

The rise in generative AI has also brought to light heightened concerns around cybersecurity, further boosting the demand for robust security solutions. AI is also increasingly being integrated into business processes and technologies, impacting everything from how healthcare professionals manage their patients, to how investors manage their portfolios. The AI market has become an economic driver that has the power to reshape the business landscape and, indeed, the overall economy. According to Puneet Chandok, President of Microsoft India, this high adoption rate reflects India’s readiness to integrate AI at scale.

India’s swift deployment of AI is driven by several distinct factors, including AI optimism in the workforce, strategic government initiatives and private sector investments. These elements create a high-growth AI environment that would be difficult to replicate elsewhere, suggesting India may capture substantial economic and corporate gains from the AI boom. Given the current AI race between the largest tech companies, we think it is unlikely that the largest investors in AI will hold back. Currently, this is feasible given their very profitable (cloud) businesses. Microsoft this week announced that cloud revenue in the second quarter rose 23% year-on-year.

or care for money

This multi-stakeholder event brought together elected and appointed officials from the UK and other countries along with academics and executives and scientists from tech and media companies. With a focus on innovation and adaptability, employers in Malaysia can successfully navigate these challenges and harness AI’s potential to drive progress and competitiveness. “AI effectively handles routine administrative tasks, enabling HR professionals to dedicate more time to meaningful, strategic work that adds value to both the organisation and its employees.”

A Microsoft report shows that 70% of workers are open to using AI to reduce workloads, and less-skilled workers see significant gains, completing tasks 35% faster. In manufacturing, AI shifts maintenance from predictive to prescriptive, enabling early issue detection and better equipment uptime. The increased use and accessibility of LLMs require enormous levels of computational power, which has led to an increase in demand for these resources. Data and security organizations are also important since they provide solutions to build the foundation of AI models through organized and secure data management capabilities.

That disastrous

decision has placed undue power in the hands of the very few ultra-wealthy. I

simply don’t think enough systems are interconnected yet, for even an extremely

sophisticated AI to completely take control. But I think there is a far greater

risk that AGI or even near-AGI presents. I’ll dive into my thoughts here, but

if you want to first learn more about the views of other experts, I wrote about warnings that Google AI

and Open AI architects provided in testimony before Congress last month.

Magid: Summit at Oxford focuses on Generative…

Recently, tech giants OpenAI and Meta have made major strides in voice and speech AI. Meta announced that their AI will respond in a voice that closely mimics celebrities and OpenAI rolled out a new API that allows users to speak with their models in real-time. AI-powered predictive maintenance has been a game changer, offering insights into potential issues with machinery before they escalate into larger, more costly problems. This article explores the transformative potential of gen AI in legal practice, while critically examining its limitations and the irreplaceable value of human expertise in delivering nuanced, high-quality legal services.

However, if current investment trajectories continue, the financial risks taken by these companies become ever-larger. This, in turn, poses an increasing risk to the financial health of these companies and a systemic risk for the tech industry. The government has not forced a

significant adjustment to technology company power since 1984 when it broke

Bell Labs into multiple smaller companies. In 1998, Microsoft’s monopoly power was challenged by Congress, but the

most significant desired changes, including a break-up of the company, never

came to bear.

Predictive maintenance systems, powered by AI, have become critical tools for reducing unplanned downtime. This has not only saved substantial maintenance costs but also extended the lifespan of essential equipment, underscoring AI’s significant impact on operational reliability. Generative AI can greatly enhance creativity by reducing the time and cost of producing new ideas and outputs, supporting various stages of the creative process. It’s essential to establish guidelines that recognise human contributions, ensuring that innovation still involves a human touch. AI’s impact on creative fields such as arts, design, and media is profound, as it can transform tasks and improve content production across formats such as text, images, and video. As the next wave of AI tools arrives, newsrooms and media companies have the opportunity to redefine their relationship with audiences.

These high-level capabilities would allow media companies to tailor their news delivery and interaction with audiences, making news consumption more personalized and immediate. The

incongruence of human interaction with capitalist-trained AI is already tearing

at the social fabric of society. Social media companies use AI to maximize

factors that deliver the greatest financial returns across those platforms. This has manifested as maximizing people’s time and level of engagement on

social media.

A door was opened somewhat for browser competition, and Microsoft

was limited in the ability to sign exclusivity deals with PC manufacturers. But

it didn’t ultimately change Microsoft’s market dominance or control. Society accepted this early technology

construct as an acceptable way to exchange a flexibly applied medium (money)

for any product or service. As society

continued to develop, the creation and regulation of currency became the domain

of government, as it is one of the fundamental technologies that is core to

civil and productive society. I would argue that AI is primarily being

deployed for two fundamental purposes – to

optimize and to maximize (or minimize). Further, both of these core applications of the technology are applied

near-universally towards capitalist principles.

From a short-term investment standpoint, companies that are developing AI technologies, building AI infrastructure such as semiconductor manufacturers or providing the necessary tools to build AI solutions are leading the charge. Over the past 70 years, artificial intelligence has evolved into a transformative social and economic force, especially with the recent rise of generative AI, creating a wealth of opportunities for investors through its rapid expansion and adoption. The spotlight on India comes at a time when many countries around the globe are keen to foster their own competing AI systems rather than turning to the U.S. or China. He pointed out that this technology has the potential to generate between 2.6 and 4.4 trillion dollars in global economic value, directly impacting sectors such as advanced manufacturing, health, banking, and logistics. The positive news about innovation is tempered by concerns about investment returns. Large language models (LLMs) have made exponential strides in recent years, but this progress has been accompanied by a corresponding exponential increase in training costs.

Of course, that’s also true for human created content, but reputable journalists and academics usually cite their sources, which doesn’t necessarily happen with AI systems. AI boosts efficiency in data-intensive industries such as financial services, scientific research, and ICT by enhancing data usability and decision-making. It increases productivity in customer support, with agents experiencing a 14% improvement in issue resolution speed.

Additionally, organizations need to monitor for data leakage at multiple inspection points, including user prompts, data retrieval and AI responses. However, this is not new to us and as I have said in previous years, African CEOs are resilient, innovative and can navigate these with a solutions-focused mindset. And navigate them we will – backed by sound business advice, strategic decision making, and a clear focus on key priorities that ensure growth over the next three years,” concludes Sehoole. Despite differing concerns on the impact of the aging workforce, retiring employees is a reality each year, and if unmanaged, will no doubt create an enormous talent risk for any organisation,” says Dr Candice Hartley, Head of People, KPMG in Africa. Similarly in the Southern Africa region, CEOs have expressed confidence in business growth in several areas. The CEOs are most concerned about the impact of economic decoupling between countries which may lead to pricing pressures over the next three years, followed by cyber security and emerging or disruptive technologies.

Antonio Novas, a senior partner at McKinsey & Company, said yesterday that the country lacks the talents to benefit from artificial intelligence (AI). Rehan Jalil is CEO of cybersecurity and data protection infrastructure firm SECURITI and ex-head of Symantec’s cloud security division. Protecting against these threats requires implementing LLM firewalls that understand natural language interactions, unlike traditional firewalls that focus on IP addresses or applications. These advanced firewalls help prevent prompt injections, jailbreak attempts and phishing attacks. Organizations must also monitor responses to prevent sensitive data leakage and ensure alignment with corporate policies on toxicity and prohibited topics.

At a global level, there is a higher recognition amongst CEOs of the imperative role that Environmental, Social, and Governance (ESG) plays in customer relationships and positive brand association when compared to African CEOs. The African CEOs believe their ESG strategies, misinformation, and reputational risk can adversely affect their business. These views varied across the three regions with the CEOs in East Africa agreeing the most. This annual report, the first of its kind to be launched in Africa by KPMG, draws on the perspectives of more than 130 CEOs from Southern, East, and West Africa regions. This follows on the back of the Global KPMG CEO Outlook Survey which celebrates its 10th edition and was conducted among 1,325 CEOs across 11 markets which examined how CEOs are looking to tackle this complex set of emerging and converging challenges. How

do we avoid a future AGI that decides that ultimate power is in the hands of

those with the strongest AI – not those with the most money?

In East Africa, the projections indicate a 5.1% expansion in 2024 with CEOs likely to take a more cautious approach when pursuing M&A, due to prevailing factors such as economic volatility and currency risk. Only 26% of CEOs expect growth through M&A because of the existing economic conditions. It isn’t

logical to think that a tool programmed to look for the lowest cost, most

optimal solution would choose such a difficult first task.

But if AGI recognizes itself as the universal tool,

currency becomes at best a source of friction and drag on systems and at worst

is perceived as a direct competitor to AGI. Algorithms will adjust to maximize

and optimize for ever-increasing compute performance rather than financial

profitability. It is about who has

the fastest and most effective AI, controlling those weapons. We are in a

cyberthreat and cyberprotection arms race that is about computing power, not

financial strength. We seek to keep humans in every critical decision loop, but

ultimately a near-AGI will recognize that for what it is.  Humans in the loop is a human desire, not a

logical optimization or maximization that algorithms are trained to recommend.

If you use Python for accessing API endpoints or web scraping, odds are you’re using either Python’s native http libraries or a third-party module like requests. In this video, we take a look at the httpx library — an easy, powerful, and future-proof way to make HTTP requests. It provides tools for everything from sending form data to handling multipart file uploads, and works with both synchronous and async code. Optimizing Field Development Through AI Models

Field development is a complex puzzle, and AI has stepped in to simplify the process. Leveraging optimization models, companies can enhance production and minimize costs simultaneously.

Given these eye-watering investments, it is no wonder that concerns about investment returns are also on the rise. Initially, we predicted 0.1 to 0.5 percentage points of additional productivity growth per year, which is at the lower end of the scale. As data flows to AI models, organizations risk losing established access entitlements. To mitigate this, companies must maintain entitlement context throughout the AI pipeline, ensuring large language models (LLMs) only access user-authorized data when generating responses. In other words, the response a user receives should be based solely on data to which they have access entitlement. Wa’ed’s new AI strategy marks another initiative by the fund in keeping with its commitment towards investing in high-potential AI applications and infrastructure players.

Generative AI Transforming Refinery Operations

Generative AI, a subset of artificial intelligence, is revolutionizing refinery processes. From crude oil distillation to product blending, AI algorithms have made operations more energy-efficient and cost-effective. By optimizing crude distillation, generative AI has reduced energy consumption and increased product yield.

During the past few months, Wa’ed Ventures announced its investment in the Korea AI chip company Rebellions, as well as the California-based startup AiXplain, a leading provider of essential infrastructure for accelerated AI development. “Artificial Intelligence models have the potential to transform businesses and everyday life profoundly. The state of readiness in organizations for impending cyberattacks is low which has prompted the act to work together to bridge the skills and cultural gap seen in many of these organizations. It is evident that growth is a key priority all around the world and buffering businesses from any impact to growth ambitions is where the focus should lie. This will align with a conservative but intentional strategic drive to sustainable growth. Geopolitical competition remains broadly inflationary with the ability to disrupt supply chains and trade investments because it shifts the focus of investment from efficiency to resilience.

Combining technology with public services, DPI has created broad access for over 900 million internet users, improving governance and payment systems, and providing a robust foundation for AI development. The Australian appetite for an AI-empowered legal profession is continuously growing, in parallel to their understanding that businesses cannot afford to sit on the sidelines. While a thoughtful approach to AI adoption is key, there are risks in going too slow.

To be best positioned to navigate this evolving landscape, investors must balance the benefits of AI-driven growth with a cautious and informed approach. Lastly, while the AI sector has attracted considerable investments over the past few years, underlying risks remain that many of these opportunities may turn out to be overvalued, especially in the short term. As we continue to see the AI industry evolve, a balanced and informed investment strategy will be key to navigating challenges and risks.

As a refresher, Generative AI (or GenAI) is artificial intelligence that can create “original” content, including text, images, video, audio and software code in response to a prompt or question entered by a human. It’s been around for a number of years but has come into prominence in the past couple of years thanks to major players like OpenAI, Google, Microsoft and Meta, which are putting massive resources into GenAI development. You can foun additiona information about ai customer service and artificial intelligence and NLP. I put original in quotes because, although the AI model generates the content, it is based on training data it gets online and from other sources. So, although the wording is original, the information comes from a great many other places.

SmartCompany is the leading online publication in Australia for free news, information and resources catering to Australia’s entrepreneurs, small and medium business owners and business managers. We aim to publish comments quickly in the interest of promoting robust conversation, but we’re a small team and we deploy filters to protect against legal risk. Occasionally your comment may be held up while it is being reviewed, but we’re working as fast as we can to keep the conversation rolling. Stay up to date with all of ING’s latest economic and financial analysis. Interestingly, this surge in patent activity was primarily led by relatively young and smaller companies, indicating that generative AI deployment fosters increased innovation.

Advanced GenAI systems rely on data, particularly the unstructured kind that constitutes up to 90% of an organization’s information landscape. The primary hurdles in enterprise AI deployment lie in safely harnessing this vast and diverse data, ensuring proper data controls and visibility, maintaining regulatory compliance, and efficiently managing AI operations at scale. Rehan Jalil is CEO of cybersecurity and data protection infrastructure firm SECURITI and ex-head of Symantec’s cloud security division. Get insights and exclusive content from the world of business and finance that you can trust, delivered to your inbox. “There is no doubt that CEOs across the continent have a myriad of growth and sustainability considerations as they continue to face universal challenges that have an impact across the continent.

• This proactive approach highlights India’s unique advantage in a world increasingly reliant on AI talent and technology integration.
• Technology, while streamlining processes, risks reducing personal interaction and engagement.
• I also looked at

  the story from the lens of

  a benevolent AGI, to contrast the risks with potential positive outcomes.

• During the past few months, Wa’ed Ventures announced its investment in the Korea AI chip company Rebellions, as well as the California-based startup AiXplain, a leading provider of essential infrastructure for accelerated AI development.
• The driving force behind this surge in training costs is the astonishing growth in computing power required by LLMs.
• Instead, it would be

  far, far simpler (and therefore meet the AI goals of maximizing efficiency) to

  simply delete all the bank accounts.

As mentioned earlier, these unique factors place India in a strong position to benefit from the AI boom. The adoption of AI in India is being used in almost every industry in the country, potentially contributing to a massive economic boost. This means that although automated the economic potential of generative ai processes can be put in place for routine tasks, the accountability for results must still reside with humans. “AI not only optimizes repetitive tasks, but allows companies to anticipate patterns, improve the supply chain, and make more informed decisions,” he said.

Newsrooms could use the Realtime API to integrate instant fact-checking into live coverage, a crucial tool for maintaining credibility in today’s environment. As reporters cover press conferences or live events, the API could flag inaccuracies in real-time, allowing journalists to correct the record almost immediately. This kind of real-time adaptability ensures that audiences are never behind the curve, making the news feel fresher and more immediate.

Conversely

– and I’ll paint with broad brush strokes – people largely achieve happiness

through relaxation, having a variety of options and by having more time, not by

rushing through things faster. We want

nicer things, which generally are more costly or have extra features, not the

lowest cost, “minimum viable products”. We love the inefficiency of spontaneous

time with friends or doing silly and simple things. We don’t define the

“perfect day” as one that efficiently packs the most productive tasks into the

shortest time with the least amount of waste. We thrive on variety and choice

and surprise, not on uniformity, standardization and rigid order. Governments, businesses, industry associations, and community groups should work together on open data initiatives and Public-Private Partnership (PPP) models.

”I suppose I should be pleased that even a bot can be self-critical when forced to reckon with a question about its own potential bias. In short, despite these challenges, Malaysia is well-positioned to address them with strategic planning and investment. By prioritising education and reskilling programmes, businesses can bridge the skills gap and prepare the workforce for AI integration. Maintaining open communication about AI’s role can alleviate ChatGPT App concerns and boost morale, and leveraging AI for economic growth while addressing infrastructure disparities can ensure more equitable benefits across the country. The rapid adoption of digital technologies, accelerated by the COVID-19 pandemic, has transformed workplaces but also raises concerns about potential dehumanisation, according to Zailani. Technology, while streamlining processes, risks reducing personal interaction and engagement.

As

AI is implemented into more and more of our everyday lives, there is a risk

that a near human-intelligent AI will

recognize that we have reached a point where money no longer needs to be the

universal tool. Today, whoever

has the most money tends to have the most power and control. Today,

anything you want to accomplish, assuming it is physically and scientifically

(or at times emotionally) possible, can be purchased if you have enough money. Money is such a strong and universally adaptable tool, that it is the

fundamental driver in our society.

In

my view, the Supreme Court should have ruled that money did not equate to free

speech itself, but rather just to how large your megaphone is for your speech. The

Constitution says nothing about protecting the volume of how loud you yell. AI-driven algorithms are not

protecting free speech, but rather institutionalizing censorship in the form of

controlling what information you see and what you don’t. Capitalism-trained AI

is a volume control knob and a content-filter, not a protection of speech. The

same argument can be made for internet search, for e-commerce offerings, for

recommendation engines and many other fundamentally AI-driven online tasks.

Airbnb-Backed Tiqets Talks AI, Profits, and Scaling Experiences

Small businesses and startups often lack a dedicated travel desk, forcing executives and founders to rely on human assistants or consuming and cumbersome travel apps. Ask Maxx, built on the AI tool Maxx Intelligence, was designed for advisors to quickly retrieve information. It analyzes data within proprietary Cruise Planners’ systems in addition to public data online, making it a more bespoke tool for franchisees. The same way I bet that people in the 1890s could never envision that in 30 years, there’ll be these manned machines in the air flying around. I think we limit ourselves sometimes to the possibilities.

In the U.S., we’ve doubled the team in the past year. The market is growing really fast there, so we’re expanding. So now we are looking into, okay, what are the next 400 destinations that we’re going to bring to full maturity? We were lucky in that sense and we’ve not wasted the crisis. We invested in a lot of things that were already on the roadmap, but I guess we accelerated that. There was just more focus on finalizing things with a little bit less pressure, like fully automated page search, fully automated creation of landing pages.

• Travel agents are still big and thriving and growing in parts like the cruise industry or complex booking.
• It was one of many examples I saw of how innovations in generative AI are impacting corporate travel.
• Over time, more people got involved and pitched in, but it was never a business-oriented thing with specific goals and timelines.

In the past couple of years, we’ve pared it back and focused on the top 200 destinations. We’ve made the model work right and proved that it’s a profitable and sustainable model. We made use of intelligence like AI and machine learning and got more productive based on that. The user can either ask the chatbot a question or select one of the suggested prompts on the right. To provide a deeper understanding of the transformative role of AI in the hospitality and travel sectors, please explore the highlights from the recent presentations at the BAE event below. For those interested in delving into the specific case studies and expert discussions, all presentations are available on demand through the event platform here.

And then over a certain period of time, some of these human functions will be taken over by tech/AI, whatever you want to call it. I don’t know if actual physical robots will come in there. Some experiments (with) hotel robots, whether it’s concierge or cleaner for the room, etc.

Meet Otto: The AI Agent Redefining Business Travel

Add in the power of GenAI, and they become industry leaders when it comes to tailoring individual trips for their clients — plus, this technology makes it easy for them to broaden their reach. This kind of unique nimbleness simply can’t be matched by larger travel companies or new travel technology startups, and it also allows them to pivot much more quickly to new market demands. Booking.com, Expedia, and several other big companies released simple chatbots powered by ChatGPT about a year ago. Those chatbots have generally existed as independent interfaces, doing little to really transform the travel planning and booking experiences as industry experts have touted. Anthropic has unveiled AI technology that could simplify travel planning and potentially disrupt online travel agencies.

And then we’re also thinking how

can we build some sort of digital ID, especially for the agent. Suppose your

agent is going and doing things, it can’t have a fingerprint about you, so if

it’s communicating with a website can it say, “This is Div’s agent or this is

Mitra’s agent,” so the website knows whose agent this is. So can you

communicate an identity to websites … and agents can chatbot for travel agency interact with one another. Our look at the most important tourism stories, including destination management, marketing, and development. Anthropic, a generative AI startup, has unveiled new tech that indicates how an AI-powered travel agent would look, writes Travel Technology Reporter Justin Dawes. Booking sites that use AI in travel booking might also see an increase in users.

Reimagining the user interface, but what about the human attention span?

And of course, they are separate companies, so they all have their own design, their own technology, their own CTOs, their own chief product… No, we are far and above where we were in 2019, before we went into the pandemic. As I mentioned, $151 billion of travel, that is a very large number. In the $130 billion market capitalization, these are enormous numbers for most companies, but it’s compared to the scale of the opportunity because travel is so big.

This method allowed us to generate tangible benefits from Al while honing our skills until we were ready to implement it for our customers. For generative Al projects in particular, we’ve found that they follow a similar cycle. It’s often pretty easy to create a basic prototype but very challenging to make it good enough for production. Ensuring the application consistently produces high-quality output can be tough, as the underlying technology is unpredictable.

We can highlight different elements on the page based on what we think the customer would find most important. Once we had these internal and support systems in place, we began making more visible changes on our platform. We started with less interactive features, like generating hotel content and review summaries, and later moved on to more interactive features like our property page Q&A bot. Progressing incrementally and responsibly is crucial; this journey will take time, but the cumulative impact on companies and consumers will be revolutionary. For example, consider filters in online travel agencies like Agoda. We have filters for price, location, size, type, etc.

What Is Otto?

You mentioned the idea that you’re going to help people with all of their travel needs, basically, wherever they are. There’s a lot happening in travel that I want to talk about, but I’m curious about the big picture. As I say, I hope a lot of people in the US — I think a lot of people in the US — know about Booking.com, and throughout the world.

A lot of people have

been using it for a lot of e-commerce. Flights has been a big one, shopping,

people have been using it for event invites, communication, LinkedIn outreach. Travel is one that keeps popping ChatGPT App up as a

big use case when we have asked users, and so that is something we are also

starting to focus a lot on. We are also thinking of launching a mobile app so

you can use the agent from your phone.

My suggestion is to first use it to streamline your operations — from initial drafts of itinerary creations to data and opportunity analysis. Kopit reports early signals from hotel earnings suggest signs of a second-half slowdown, adding the picture will be clearer when IHG, Hyatt and Hilton, among other companies, report this week. However, cruise executives said they haven’t seen any slowdown in bookings and guest spending. “Overall, the short answer is no cracks, no deterioration,” said the chief financial officer of Norwegian Cruise Line. Travel executives see activities and experiences as increasingly lucrative, and here’s what the numbers say about how travelers are spending on them.

Start with a clear goal that translates into a metric. This will give you the time and guidance to focus on what you do best — serving travelers. The next thing you need to know is where in the travel process to use GenAI.

Whether written or verbal, AI can translate any language into another without manually inputting any text. Translation apps — such as Google Translate — can also use augmented ChatGPT reality (AR) to help translate text. When a device’s camera is pointed to a block of text, trained AI can quickly translate the words into the user’s desired language.

Learning new things and transforming business operations always means a series of challenges and issues, but GenAI also represents an amazing opportunity that goes well beyond the simplistic “innovate or die” aphorism. This technology simply can’t be ignored, and that’s especially true for smaller businesses — they need to embrace GenAI early and make sure they do it the right way. In the airline sector, Delta and United have gotten a boost from the rise in premium long-haul demand fueled by travelers more willing to spend freely. As of the second quarter, that support was still there. But if there is a recession, it could benefit low-cost carriers since they do well when budgets are tight.

MasterCard – Trends and Innovations in Tourism

Chatbots and virtual assistants have become an essential part of the customer service world and can often help improve customer satisfaction. According to a study from Tidio, 62% of customers say they would rather use an online chatbot than wait for human assistance. While the technology is still evolving, McKinsey advises business leaders to begin exploring how AI agents could enhance their operations. McKinsey’s recent “State of AI” survey found that 72% of companies are already deploying AI solutions, with interest in gen AI rapidly increasing. Businesses should prepare by codifying key workflows, planning their tech infrastructure, and implementing human-in-the-loop control mechanisms to manage risks and validate AI outputs. But Booking Holdings CEO Glenn Fogel thinks that growth will fade fast and that AI will accelerate a decline in traditional travel agents.

I feel like in AI, we are in that phase — only more exponential. But we are still in the initial promises phase and actual changes are still to come. To simplify booking and sell more Round the World tickets, Oneworld turned to Elemental Cognition, an AI startup founded by David Ferrucci, a computer scientist who led the development of IBM’s Watson computer. Expedia also said it would launch a cross-date price comparison tool, an AI-powered help page, and guest review summaries as part of its spring update. Compared to Europe and the United States, Asia is much more diverse across its different regions and countries.

Good engineering always begins with understanding the problem. Generative Al opens so many new doors that it requires a re-evaluation of where technology can be helpful — you need to remap your problems to solutions. For example, scanning legal contracts for specific concerns at scale was something we wouldn’t have considered using technology for in the past, but now it’s possible. Technology has always been a foundational priority at Agoda, no more so than since the ascent of Omri Morgenshtern as CEO two years ago. Mogenshtern and Zalzberg were co-founders of Qlika, which specialized in online marketing optimization and was acquired in 2014 by Booking Holdings.

Software + Service

It just seems strange to me, and that’s a rule that’s not good. Of course, politics plays a big role in a lot of this. But if you want a home, we can provide you with a home, too.

For all the promise of large language models, they are ingesting a lot of the garbage created in the past 20 years from SEO-driven travel content and bad writing, then regurgitating it back to us with hallucinations and all. Colin Nagy is a marketing strategist and writes on customer-centric experiences and innovation across the luxury sector, hotels, aviation, and beyond. Can we make use of existing systems so the agent can also focus on that.

It only showed general information about destinations and their events. Ideally, it would show specific information about when those events would occur and then show flight and hotel information based on those dates. While the business models for Despegar and Kayak are different, the new tools give us a better understanding of how the future could look. Altour’s new product, AI Transform, is a tool meant to help with travel program compliance.

‘There’s no price’ Microsoft could pay Apple to use Bing: all the spiciest parts of the Google antitrust ruling

Otto has been designed as a virtual travel agent for planning and booking business trips, with the ability to provide support during trips if flights or plans change. Powered by the latest generative AI models, users will be able to prompt a search with natural language. Anthropic, a generative AI startup and competitor to OpenAI, has introduced a new AI feature designed to mimic human travel agents by performing tasks such as moving a cursor and typing. This technology has the potential to automate travel planning, potentially bypassing traditional online travel agencies and transforming how travel bookings are made. While the feature is still in beta and has some bugs, demonstrations have shown its capability to complete tasks like finding directions and setting calendar events.

One’s a factor of us being bigger; one’s part of it because, as you point out, the world has changed a little bit, and it does take time. And it’s thinking these things through and dealing with lawyers and people who are [in the] public affairs field. We never had a public affairs department until relatively recently, and our legal department’s expanded a great deal. Part of the problem, though, is that we prefer to spend that money on hiring engineers and create better services.

Otto’s AI capabilities are at the forefront of what’s possible. I couldn’t be more excited to partner with the incredible team at Madrona Venture Labs and Otto CEO Michael Gulmann to bring Otto to the market. We predict a significant leap in AI applications, particularly in the travel industry. While chatbots have become commonplace, we foresee a broader spectrum where AI extends its influence across diverse travel scenarios. You can foun additiona information about ai customer service and artificial intelligence and NLP. Beyond the conventional role of generating itineraries, TripGenie seamlessly integrates with on-site business operations like flight or hotel bookings. This means going beyond merely suggesting travel plans to facilitating in-site business reservations and integrating user travel needs from start to end.

At present, our inquiries can be broadly categorized into three types. The first category involves reservations for flights, hotels and other services, allowing users to swiftly book them after engaging with TripGenie. The second category pertains to itinerary-related inquiries.

So, I know there are going to be some soft times, there are going to be some great times. Like when we came out of the pandemic, there was that revenge travel surge, which is fantastic. But the truth is, I know that that couldn’t possibly last because in the end, we’re going to end up in a long-term run where travel goes slightly better than GDP. Now, on top of that, our job is to get a bigger share of that, and we have benefits of scale and capabilities that enable us to do that.

By leveraging your data on loyalty programs, credit card benefits, and insurance coverage, AI agents will be able to craft highly tailored travel plans, negotiate on your behalf and even decide which card to use to book to maximize points. Their role will extend beyond the initial booking, ensuring seamless journeys and swift resolutions to unexpected challenges. Whatever helps take the stress out of planning travel especially with groups or families and brings in more joy when things go awry is not only part of the experience but well needed relief. “The reason behind a large set of business travel being unmanaged is that services like Concur or other travel management companies are too expensive for small businesses. Typically, small business owners take the help of executive assistants for travel. That’s what’s good about Otto, it acts as your own executive assistant or a travel agent,” he said.